CN113743973B - Method and device for analyzing market hotspot trend - Google Patents
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Abstract
The invention discloses a method and a device for analyzing market hotspot trend, and relates to the technical field of computers. One embodiment of the method comprises the following steps: acquiring a first keyword library for describing the attention of a user and a second keyword library for describing articles; according to the first keyword library and the second keyword library, selecting an emerging keyword from the first keyword library, wherein the emerging keyword is a first keyword with the current market ratio of the article being smaller than a set threshold value; and analyzing the search amount of the emerging keywords within a set time length in the future to obtain market hotspot trends. According to the embodiment, under the condition that sampling deviation is not generated rapidly, the emerging trend of the product can be predicted, the market hot spot trend is analyzed, and the analysis result is more timely, comprehensive and accurate.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for analyzing market hotspot trends.
Background
Analysis and prediction of emerging market trends can help manufacturers to know the properties of commodities which can become market hotspots in the future, so that the commodities can be laid out in advance, and the commodities with the properties are configured to assist the manufacturers to become explosive better. The existing market hotspot trend analysis and prediction method is mainly characterized in that the preference degree of a user for a specific commodity attribute is known through manual user investigation, and then final trend judgment is given based on the result of investigation analysis.
However, the existing user investigation method has the problems of long period and limited coverage range, and the emerging trend becomes a hot spot after the investigation result comes out, so that the advanced layout of manufacturers and merchants loses meaning, and the analysis and prediction result of the market hot spot trend is not timely enough and has hysteresis; on the other hand, the investigation can only cover a part of quite small user groups, and the part of groups can not reflect the whole situation of the market, so that analysis and prediction results of market hot spot trend are not comprehensive and accurate enough.
Disclosure of Invention
Therefore, the embodiment of the invention provides a method and a device for analyzing market hotspot trend, which can achieve the purposes of predicting the emerging trend of products and further analyzing the market hotspot trend under the condition of being rapid and not having sampling deviation, and the analysis result is more timely, comprehensive and accurate.
To achieve the above object, according to one aspect of the embodiments of the present invention, there is provided a method of analyzing market hotspot trends.
A method of analyzing market hotspot trends, comprising:
Acquiring a first keyword library for describing the attention of a user and a second keyword library for describing articles;
According to the first keyword library and the second keyword library, selecting an emerging keyword from the first keyword library, wherein the emerging keyword is a first keyword with the current market ratio of an article being smaller than a set threshold value;
and analyzing the search amount of the emerging keywords within a set time length in the future to obtain market hotspot trends.
Optionally, obtaining a first keyword library for describing a user interest level and a second keyword library for describing an item includes:
performing word segmentation processing on data related to the user attention to obtain a first word segmentation set, and performing aggregation processing according to the similarity of words in the first word segmentation set to obtain a first keyword library for describing the user attention;
And carrying out word segmentation processing on the article attribute data to obtain a second word segmentation set, and carrying out aggregation processing according to the similarity of words in the second word segmentation set to obtain a second keyword library for describing the articles.
Optionally, selecting the emerging keyword from the first keyword library includes:
obtaining the similarity of each first keyword in the first keyword library and each second keyword in the second keyword library;
For each first keyword, taking the first keyword as a first matching keyword, taking a second keyword, the similarity of which with the first keyword meets a set first threshold, as a second matching keyword, and establishing an association relationship between the first matching keyword and the second matching keyword;
Searching an article according to the second matching keyword, and acquiring the market occupation ratio of the article;
and according to the association relation, acquiring a first matching keyword corresponding to the article with the market occupation ratio smaller than the set threshold value as the emerging keyword.
Optionally, obtaining the similarity of each first keyword in the first keyword library and each second keyword in the second keyword library includes:
judging whether the first keywords are contained in the second keyword library for each first keyword in the first keyword library;
if not, calculating the similarity between the first keyword and each second keyword in the second keyword library.
Alternatively, the similarity between keywords is obtained by:
obtaining semantic vectors of the keywords by using a language matching model;
and calculating the similarity between the keywords based on the semantic vectors.
Optionally, the search amount analysis of the emerging keywords within the future set time period includes:
And analyzing the search amount of the emerging keywords within a set time length in the future through a time sequence prediction model.
According to another aspect of the embodiment of the invention, an apparatus for analyzing market hotspot trends is provided.
An apparatus for analyzing market hotspot trends, comprising:
The data acquisition module is used for acquiring a first keyword library for describing the attention of a user and a second keyword library for describing the objects;
the keyword selection module is used for selecting an emerging keyword from the first keyword library according to the first keyword library and the second keyword library, wherein the emerging keyword is a first keyword with the current market ratio of the article being smaller than a set threshold value;
and the search amount analysis module is used for carrying out search amount analysis on the emerging keywords within a set time length in the future so as to obtain market hotspot trends.
Optionally, the data acquisition module is further configured to:
performing word segmentation processing on data related to the user attention to obtain a first word segmentation set, and performing aggregation processing according to the similarity of words in the first word segmentation set to obtain a first keyword library for describing the user attention;
And carrying out word segmentation processing on the article attribute data to obtain a second word segmentation set, and carrying out aggregation processing according to the similarity of words in the second word segmentation set to obtain a second keyword library for describing the articles.
Optionally, the keyword selection module is further configured to:
obtaining the similarity of each first keyword in the first keyword library and each second keyword in the second keyword library;
For each first keyword, taking the first keyword as a first matching keyword, taking a second keyword, the similarity of which with the first keyword meets a set first threshold, as a second matching keyword, and establishing an association relationship between the first matching keyword and the second matching keyword;
Searching an article according to the second matching keyword, and acquiring the market occupation ratio of the article;
and according to the association relation, acquiring a first matching keyword corresponding to the article with the market occupation ratio smaller than the set threshold value as the emerging keyword.
Optionally, the keyword selection module is further configured to:
judging whether the first keywords are contained in the second keyword library for each first keyword in the first keyword library;
if not, calculating the similarity between the first keyword and each second keyword in the second keyword library.
Alternatively, the similarity between keywords is obtained by:
obtaining semantic vectors of the keywords by using a language matching model;
and calculating the similarity between the keywords based on the semantic vectors.
Optionally, the search amount analysis module is further configured to:
And analyzing the search amount of the emerging keywords within a set time length in the future through a time sequence prediction model.
According to yet another aspect of an embodiment of the present invention, an electronic device for analyzing market hotspot trends is provided.
An electronic device for analyzing market hotspot trends, comprising: one or more processors; and the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors are enabled to realize the method for analyzing the market hotspot trend provided by the embodiment of the invention.
According to yet another aspect of an embodiment of the present invention, a computer-readable medium is provided.
A computer readable medium having stored thereon a computer program which when executed by a processor implements a method of analyzing market hotspot trends provided by an embodiment of the present invention.
One embodiment of the above invention has the following advantages or benefits: acquiring a first keyword library for describing the attention of a user and a second keyword library for describing an article; according to the first keyword library and the second keyword library, selecting an emerging keyword from the first keyword library, wherein the emerging keyword is a first keyword with the current market ratio of the article being smaller than a set threshold value; the method comprises the following steps of carrying out search quantity analysis on emerging keywords within a set time length in the future to obtain market hotspot trend, converting the prediction of the emerging hotspot trend of the product market into the prediction of the search trend of the focus of the user, and predicting the future behaviors of the whole users on a certain keyword through a deep learning model, so that the aim of predicting the emerging trend of the product and further analyzing the market hotspot trend is fulfilled under the condition that sampling deviation does not occur rapidly, and the analysis result is more timely, comprehensive and accurate.
Further effects of the above-described non-conventional alternatives are described below in connection with the embodiments.
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The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of the main steps of a method of analyzing market hotspot trends in accordance with an embodiment of the present invention;
FIG. 2 is a system framework diagram of analyzing market hotspot trends in accordance with one embodiment of the present invention;
FIG. 3 is a schematic diagram of the implementation of a search volume prediction model according to one embodiment of the present invention;
FIG. 4 is a schematic diagram of the main modules of an apparatus for analyzing market hotspot trends according to an embodiment of the present invention;
FIG. 5 is an exemplary system architecture diagram in which embodiments of the present invention may be applied;
Fig. 6 is a schematic diagram of a computer system suitable for use in implementing an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present invention are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In order to solve the technical problems in the prior art, the invention provides a method for analyzing market hotspot trend, which converts the prediction of the emerging hotspot trend of the product market into the prediction of the search trend of the focus of the user, predicts the future behaviors of the total users on a certain keyword through a deep learning model, thereby achieving the purposes of predicting the emerging trend of the product and further analyzing the market hotspot trend under the condition of rapid and no sampling deviation, and the analysis result is more timely, comprehensive and accurate.
FIG. 1 is a schematic diagram of the main steps of a method of analyzing market hotspot trends according to an embodiment of the present invention. As shown in fig. 1, the method for analyzing market hotspot trend according to the embodiment of the present invention mainly includes the following steps S101 to S103.
Step S101: acquiring a first keyword library for describing the attention of a user and a second keyword library for describing articles;
Step S102: according to the first keyword library and the second keyword library, selecting an emerging keyword from the first keyword library, wherein the emerging keyword is a first keyword with the current market ratio of the article being smaller than a set threshold value;
Step S103: and analyzing the search amount of the emerging keywords within a set time length in the future to obtain market hotspot trends.
According to one embodiment of the invention, when the first keyword library for describing the user attention is acquired, the data related to the user attention can be subjected to word segmentation processing to obtain a first word segmentation set, and aggregation processing is performed according to the similarity of words in the first word segmentation set to obtain the first keyword library for describing the user attention. Specifically, first, data related to the user's attention is acquired, for example: search data, question-answer data, comment data, etc., and then word-segmenting the data, in particular, sentences included in the data may be divided into a plurality of words by a commonly used word-segmentation tool (for example: jieba word-segmentation), which constitute the first word-segmentation set. Then, the words with the same meaning are subjected to aggregation processing to obtain a first keyword library, specifically, when the words are subjected to aggregation processing, semantic vectors of the words are obtained through a language matching model (for example, a BERT model is a new word2Vec which is used for generating a related model of word vectors), similarity among the words is calculated based on the distance among the semantic vectors, the words with the similarity reaching a certain threshold value can be aggregated into the same word to serve as a first keyword, and finally the first keyword library A for describing the attention of a user is obtained.
Similarly, when the second keyword library for describing the article is obtained, the article attribute data may be subjected to word segmentation processing to obtain a second word set, and aggregation processing is performed according to the similarity of the words in the second word set to obtain the second keyword library for describing the article. Specifically, the item attribute data is, for example, a commodity title, commodity detailed description information, or the like. Firstly, acquiring article attribute data, then, word segmentation is carried out on the article attribute data, and then, aggregation treatment is carried out, so that a second keyword library B for describing articles can be obtained.
After the first keyword library A and the second keyword library B are obtained, the emerging keywords can be selected from the first keyword library A through comparison and analysis of the first keyword library A and the second keyword library B. According to the technical scheme of the invention, the emerging keywords refer to keywords with relatively smaller current market proportion of articles, namely, the current market proportion of articles related to the emerging keywords is relatively smaller in all first keywords included in the first keyword library A, and the users pay attention to the keywords, but pay less attention to the articles or pay less attention to the articles, so that the articles are likely to become hot spots of future markets.
According to one embodiment of the present invention, when an emerging keyword is selected from the first keyword library, the method specifically includes the following steps:
obtaining the similarity of each first keyword in the first keyword library and each second keyword in the second keyword library;
For each first keyword, taking the first keyword as a first matching keyword, taking a second keyword, the similarity of which with the first keyword meets a set similarity threshold, as a second matching keyword, and establishing an association relation between the first matching keyword and the second matching keyword;
searching for the object according to the second matching keyword, and acquiring the market ratio of the object;
And according to the association relation, acquiring a first matching keyword corresponding to the article with the market occupation ratio smaller than the set threshold value as the emerging keyword.
In a specific embodiment, assuming that the similarity between a certain first keyword A1 in the first keyword library a and two second keywords B1 and B2 in the second keyword library B reaches 90% and 85% respectively, which exceed a preset similarity threshold value of 80%, the first keyword A1 and the second keywords B1 and B2 are used as a group of first keywords and second keywords whose similarity satisfies the preset similarity threshold value, the first keyword A1 is used as a first matching keyword, the second keywords B1 and B2 are used as second matching keywords, and an association relationship between the first matching keywords and the second matching keywords is established. In particular, the second matching keyword associated with a certain first matching keyword may include one second keyword or may include a plurality of second keywords. And if the similarity between the first keyword A2 in the first keyword library A and any one of the second keywords in the second keyword library B is smaller than the set similarity threshold, the first keyword A2 is an invalid first keyword.
After the association relation between the first matching keywords and the second matching keywords is established, the corresponding articles can be searched according to the second matching keywords, and the market occupation ratio of the articles is obtained. In this embodiment, for the second matching keywords B1 and B2 associated with the first matching keyword A1, the comparison matching is performed using B1 and B2 and the item attribute data, respectively, the item corresponding to the item attribute data in which B1 or B2 is included is taken as the found item, and then the market ratio of these items is calculated. Wherein, when calculating the market ratio, the number of the articles can be calculated according to the following steps: the number M of item attribute data is compared to the total number N of items of the same type. For example: assuming that the article attribute data is article titles, the article titles contain the article titles of B1 or B2, the number of which is M, and the total number of the same type of articles is N, the market ratio of the articles is M/N. Similarly, when calculating the market ratio, the number of these items may be: the number M of item attribute data serves as the market ratio of these items. The manner of calculating the market ratio is not limited to the manner listed in the embodiments of the present invention, and may be flexibly selected according to the application requirements, which is not limited by the present invention.
After the market proportion of the articles is obtained, the articles with the market proportion smaller than the set threshold value can be obtained, and the second matching keywords of the articles can be further obtained. And obtaining corresponding first matching keywords according to the association relation between the first matching keywords and the second matching keywords, and taking the first matching keywords as emerging keywords. After the market ratio of the articles is obtained, the market ratio of the articles can be ranked from small to large, and the first matching keywords corresponding to the articles with the specified number (for example, 500) in the front ranking can be used as the emerging keywords.
According to one embodiment, when obtaining the similarity between each first keyword in the first keyword library and each second keyword in the second keyword library, the method specifically may include:
Judging whether the first keywords are contained in the second keyword library for each first keyword in the first keyword library;
if not, calculating the similarity of the first keywords and each second keyword in the second keyword library.
In the implementation, for each first keyword in the first keyword library, whether the same second keyword exists in the second keyword library can be firstly searched, if so, the similarity between the first keyword and the second keyword in the second keyword library is 100%, and at this time, the similarity between the first keyword and other second keywords in the second keyword library is not required to be calculated. Then, the first keyword and the second keyword can be used as a first matching keyword and a second matching keyword to execute subsequent operations. Otherwise, if the second keyword library does not include the first keyword, the similarity between the first keyword and each second keyword in the second keyword library needs to be calculated.
According to the technical scheme of the invention, the similarity between the keywords can be obtained specifically by the following modes:
obtaining semantic vectors of the keywords by using a language matching model;
Similarity between keywords is calculated based on the semantic vector.
Here, the similarity between the keywords may be a similarity between the first keyword and the second keyword. Semantic vectors of the respective keywords are obtained using a language matching model (e.g., BERT model, essentially new word2Vec, a correlation model for generating word vectors), and then the similarity between the keywords is calculated based on the distance between the semantic vectors.
According to the embodiment of the invention, when the search amount analysis is performed on the emerging keywords within the future set time length, the search amount analysis can be performed on the emerging keywords within the future set time length through the time sequence prediction model. The time series prediction model is, for example, an LSTM (long-short term memory ) model, an ARMA (Autoregressive moving average model, autoregressive moving average model) model, an ARIMA (Autoregressive Integrated Moving Average model, differential integration moving average autoregressive model) model, or the like.
FIG. 2 is a system framework diagram of analyzing market hotspot trends in accordance with one embodiment of the present invention. In the embodiment of the present invention, taking analysis of commodity data collected by an e-commerce platform as an example, as shown in fig. 2, a system for analyzing market hotspot trend according to one embodiment of the present invention mainly includes three major modules: the system comprises a keyword generation module, an emerging keyword generation module and a search amount prediction module. The functions of the modules are as follows:
1. A keyword generation module, configured to generate, for data related to a user attention, for example: the method comprises the steps of carrying out word segmentation processing on commodity question-answer data, historical search data, search quantity data, historical comment data and the like, and then aggregating to obtain a first keyword library A for describing the attention of a user; and for item attribute data, such as: the commodity title, commodity detailed information and the like are subjected to word segmentation processing and are aggregated to obtain a second keyword library B for describing the objects;
2. The emerging keyword generation module is used for matching the first keyword library A used for describing the attention of the user and the second keyword library B used for describing the objects, which are generated in the keyword generation module, and selecting the first keywords corresponding to the commodities with relatively smaller current market occupation ratio as emerging keywords according to the occupation ratio condition of the commodities in the market. Specifically, for each first keyword in the first keyword library a, judging whether the first keyword is contained in the second keyword library; if not, calculating the similarity of each second keyword in the first keyword and the second keyword library, so as to obtain the similarity of each first keyword in the first keyword library A and each second keyword in the second keyword library B; for each first keyword, taking the first keyword as a first matching keyword, taking a second keyword, the similarity of which with the first keyword meets a set similarity threshold, as a second matching keyword, and establishing an association relationship between the first matching keyword and the second matching keyword; searching for the object according to the second matching keyword, and acquiring the market ratio of the object; according to the association relation, obtaining a first matching keyword corresponding to an article with the market occupation ratio smaller than a set threshold value as an emerging keyword;
3. The search amount prediction module is used for predicting the future search amount of the emerging keywords obtained by the excavation of the emerging keyword generation module, so that the emerging trend and the market hot spot trend of the product are judged, and an auxiliary decision is provided for customizing the product. In one embodiment of the present invention, the search amount of a keyword (flexibly settable) in the next three months is predicted using the search amount of the keyword in the last year and half (flexibly settable), the search user portraits, the market macro economic data, and the like as training data. Specifically: for a certain keyword k, assume that the time starting point is t, and the total search amount of three consecutive months after t is The data of user figures (sex proportion, average age, average price of customers, etc.), market macro economy, etc. related to the keyword k three months before t are taken asThereby constructing a training set. And training based on a time sequence prediction model LSTM model by utilizing the training set to obtain a search quantity prediction model, and then using the search quantity prediction model to analyze and predict market hotspot trend.
FIG. 3 is a schematic diagram of the implementation of a search volume prediction model according to one embodiment of the present invention. As shown in fig. 3, the search amount prediction model is trained based on a time series prediction model LSTM model. In an embodiment of the invention, to AndFor input toFor output, updating parameters of the LSTM model based on the training set to finally obtain a search quantity prediction model, so that the search quantity prediction model can predict a keyword i to be predicted
Fig. 4 is a schematic diagram of main modules of an apparatus for analyzing market hotspot trends according to an embodiment of the present invention. As shown in fig. 4, an apparatus 400 for analyzing market hotspot trend according to an embodiment of the present invention mainly includes a data acquisition module 401, a keyword selection module 402, and a search amount analysis module 403.
A data acquisition module 401, configured to acquire a first keyword library for describing a user attention and a second keyword library for describing an item;
A keyword selection module 402, configured to select an emerging keyword from the first keyword library according to the first keyword library and the second keyword library, where the emerging keyword is a first keyword with a current market ratio of an article being less than a set threshold;
And the search amount analysis module 403 is configured to perform search amount analysis on the emerging keywords within a set time period in the future to obtain a market hotspot trend.
According to one embodiment of the invention, the data acquisition module 401 may also be configured to:
performing word segmentation processing on data related to the user attention to obtain a first word segmentation set, and performing aggregation processing according to the similarity of words in the first word segmentation set to obtain a first keyword library for describing the user attention;
And carrying out word segmentation processing on the article attribute data to obtain a second word segmentation set, and carrying out aggregation processing according to the similarity of words in the second word segmentation set to obtain a second keyword library for describing the articles.
According to another embodiment of the present invention, the keyword extraction module 402 may be further configured to:
obtaining the similarity of each first keyword in the first keyword library and each second keyword in the second keyword library;
For each first keyword, taking the first keyword as a first matching keyword, taking a second keyword, the similarity of which with the first keyword meets a set first threshold, as a second matching keyword, and establishing an association relationship between the first matching keyword and the second matching keyword;
Searching an article according to the second matching keyword, and acquiring the market occupation ratio of the article;
and according to the association relation, acquiring a first matching keyword corresponding to the article with the market occupation ratio smaller than the set threshold value as the emerging keyword.
According to yet another embodiment of the present invention, the keyword selection module 402 may be further configured to:
judging whether the first keywords are contained in the second keyword library for each first keyword in the first keyword library;
if not, calculating the similarity between the first keyword and each second keyword in the second keyword library.
According to yet another embodiment of the present invention, the similarity between keywords is obtained by:
obtaining semantic vectors of the keywords by using a language matching model;
and calculating the similarity between the keywords based on the semantic vectors.
According to yet another embodiment of the present invention, the search volume analysis module 403 may be further configured to:
And analyzing the search amount of the emerging keywords within a set time length in the future through a time sequence prediction model.
According to the technical scheme of the embodiment of the invention, a first keyword library for describing the attention of a user and a second keyword library for describing the objects are obtained; according to the first keyword library and the second keyword library, selecting an emerging keyword from the first keyword library, wherein the emerging keyword is a first keyword with the current market ratio of the article being smaller than a set threshold value; the method comprises the following steps of carrying out search quantity analysis on emerging keywords within a set time length in the future to obtain market hotspot trend, converting the prediction of the emerging hotspot trend of the product market into the prediction of the search trend of the focus of the user, and predicting the future behaviors of the whole users on a certain keyword through a deep learning model, so that the aim of predicting the emerging trend of the product and further analyzing the market hotspot trend is fulfilled under the condition that sampling deviation does not occur rapidly, and the analysis result is more timely, comprehensive and accurate.
FIG. 5 illustrates an exemplary system architecture 500 of a method of analyzing market hotspot trends or an apparatus for analyzing market hotspot trends to which embodiments of the present invention may be applied.
As shown in fig. 5, the system architecture 500 may include terminal devices 501, 502, 503, a network 504, and a server 505. The network 504 is used as a medium to provide communication links between the terminal devices 501, 502, 503 and the server 505. The network 504 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
A user may interact with the server 505 via the network 504 using the terminal devices 501, 502, 503 to receive or send messages or the like. Various communication client applications such as shopping class applications, web browser applications, search class applications, etc. (by way of example only) may be installed on the terminal devices 501, 502, 503.
The terminal devices 501, 502, 503 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 505 may be a server providing various services, such as a background management server (by way of example only) that provides support for market hotspot trend query requests sent by users using the terminal devices 501, 502, 503. The background management server can perform according to the received market hotspot trend query request: acquiring a first keyword library for describing the attention of a user and a second keyword library for describing articles; according to the first keyword library and the second keyword library, selecting an emerging keyword from the first keyword library, wherein the emerging keyword is a first keyword with the current market ratio of an article being smaller than a set threshold value; and carrying out processing such as search amount analysis and the like in a set time length in the future on the emerging keywords, and feeding back processing results (such as market hot spot trend or emerging product trend-only as an example) to the terminal equipment.
It should be noted that, the method for analyzing the trend of the market hotspot provided in the embodiment of the present invention is generally executed by the server 505, and accordingly, the device for analyzing the trend of the market hotspot is generally disposed in the server 505.
It should be understood that the number of terminal devices, networks and servers in fig. 5 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 6, there is illustrated a schematic diagram of a computer system 600 suitable for use in implementing a terminal device or server in accordance with an embodiment of the present invention. The terminal device or server shown in fig. 6 is only an example, and should not impose any limitation on the functions and scope of use of the embodiments of the present invention.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU) 601, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, mouse, etc.; an output portion 607 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The drive 610 is also connected to the I/O interface 605 as needed. Removable media 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on drive 610 so that a computer program read therefrom is installed as needed into storage section 608.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 609, and/or installed from the removable medium 611. The above-described functions defined in the system of the present invention are performed when the computer program is executed by a Central Processing Unit (CPU) 601.
The computer readable medium shown in the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units or modules involved in the embodiments of the present invention may be implemented in software or in hardware. The described units or modules may also be provided in a processor, for example, as: a processor comprises a data acquisition module, a keyword selection module and a search amount analysis module. Where the names of the units or modules do not constitute a limitation of the units or modules themselves in some cases, for example, the data acquisition module may also be described as "a module for acquiring a first keyword library for describing the user's attention and a second keyword library for describing the item".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be present alone without being fitted into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to include: acquiring a first keyword library for describing the attention of a user and a second keyword library for describing articles; according to the first keyword library and the second keyword library, selecting an emerging keyword from the first keyword library, wherein the emerging keyword is a first keyword with the current market ratio of an article being smaller than a set threshold value; and analyzing the search amount of the emerging keywords within a set time length in the future to obtain market hotspot trends.
According to the technical scheme of the embodiment of the invention, a first keyword library for describing the attention of a user and a second keyword library for describing the objects are obtained; according to the first keyword library and the second keyword library, selecting an emerging keyword from the first keyword library, wherein the emerging keyword is a first keyword with the current market ratio of the article being smaller than a set threshold value; the method comprises the following steps of carrying out search quantity analysis on emerging keywords within a set time length in the future to obtain market hotspot trend, converting the prediction of the emerging hotspot trend of the product market into the prediction of the search trend of the focus of the user, and predicting the future behaviors of the whole users on a certain keyword through a deep learning model, so that the aim of predicting the emerging trend of the product and further analyzing the market hotspot trend is fulfilled under the condition that sampling deviation does not occur rapidly, and the analysis result is more timely, comprehensive and accurate.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives can occur depending upon design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (8)
1. A method of analyzing market hotspot trends, comprising:
Acquiring a first keyword library for describing the attention of a user and a second keyword library for describing articles;
According to the first keyword library and the second keyword library, selecting an emerging keyword from the first keyword library, wherein the emerging keyword is a first keyword with the current market ratio of an article being smaller than a set threshold value;
analyzing the search amount of the emerging keywords within a set time length in the future to obtain market hotspot trends;
The selecting the new keywords from the first keyword library comprises the following steps: obtaining the similarity of each first keyword in the first keyword library and each second keyword in the second keyword library; for each first keyword, taking the first keyword as a first matching keyword, taking a second keyword, the similarity of which with the first keyword meets a set similarity threshold, as a second matching keyword, and establishing an association relationship between the first matching keyword and the second matching keyword; searching an article according to the second matching keyword, and acquiring the market occupation ratio of the article; and according to the association relation, acquiring a first matching keyword corresponding to the article with the market occupation ratio smaller than the set threshold value as the emerging keyword.
2. The method of claim 1, wherein obtaining a first keyword library for describing a user interest and a second keyword library for describing an item comprises:
performing word segmentation processing on data related to the user attention to obtain a first word segmentation set, and performing aggregation processing according to the similarity of words in the first word segmentation set to obtain a first keyword library for describing the user attention;
And carrying out word segmentation processing on the article attribute data to obtain a second word segmentation set, and carrying out aggregation processing according to the similarity of words in the second word segmentation set to obtain a second keyword library for describing the articles.
3. The method of claim 1, wherein obtaining the similarity of each first keyword in the first keyword library and each second keyword in the second keyword library comprises:
judging whether the first keywords are contained in the second keyword library for each first keyword in the first keyword library;
if not, calculating the similarity between the first keyword and each second keyword in the second keyword library.
4. A method according to claim 1 or 3, wherein the similarity between keywords is obtained by:
obtaining semantic vectors of the keywords by using a language matching model;
and calculating the similarity between the keywords based on the semantic vectors.
5. The method of claim 1, wherein the search volume analysis of the emerging keywords for a set length of time in the future comprises:
And analyzing the search amount of the emerging keywords within a set time length in the future through a time sequence prediction model.
6. An apparatus for analyzing market hotspot trends, comprising:
The data acquisition module is used for acquiring a first keyword library for describing the attention of a user and a second keyword library for describing the objects;
the keyword selection module is used for selecting an emerging keyword from the first keyword library according to the first keyword library and the second keyword library, wherein the emerging keyword is a first keyword with the current market ratio of the article being smaller than a set threshold value;
The search amount analysis module is used for carrying out search amount analysis on the emerging keywords within a set time length in the future so as to obtain market hotspot trends;
The keyword selection module is further configured to: obtaining the similarity of each first keyword in the first keyword library and each second keyword in the second keyword library; for each first keyword, taking the first keyword as a first matching keyword, taking a second keyword, the similarity of which with the first keyword meets a set similarity threshold, as a second matching keyword, and establishing an association relationship between the first matching keyword and the second matching keyword; searching an article according to the second matching keyword, and acquiring the market occupation ratio of the article; and according to the association relation, acquiring a first matching keyword corresponding to the article with the market occupation ratio smaller than the set threshold value as the emerging keyword.
7. An electronic device for analyzing market hotspot trends, comprising:
One or more processors;
storage means for storing one or more programs,
When executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-5.
8. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-5.
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| US9002892B2 (en) * | 2011-08-07 | 2015-04-07 | CitizenNet, Inc. | Systems and methods for trend detection using frequency analysis |
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