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2011
Information Gain Clustering through Roulette Wheel Genetic Algorithm (IGCRWGA) is a novel heuristic used in Recommender System (RS) for solving personalization problems. In a bid to generate information on the behavior and effects of Roulette Wheel Genetic Algorithm (RWGA) in Recommender System (RS) used in personalization of cold start problem, IGCRWGA is developed and experimented upon in this work / paper. A comparison with other heuristics for personalization of cold start problem - such as Information Gain Clustering Neighbor through Bisecting K-Mean Algorithm (IGCN), Information Gain Clustering through Genetic Algorithm (GCEGA), among others -- showed that IGCRWGA produced the best recommendation for large recommendation size (i.e. greater than 30 items) since it is associated with the least Mean Absolute Error (MAE), the evaluation metric used in this work.
2011 •
IGCEGA, an acronym for Information Gain Clustering through Elitizt Genetic Algorithm, is a novel heuristic used in Recommender System (RS) for solving personalization problems. In comparison with IGCGA (Information Gain Clustering through Genetic Algorithm), IGCEGA is not associated with the inherent problem of increasing the possibility of losing good solution during the crossover phase, which translates into increasing the guarantee of converging to a global minima and consequently, enhancing the accuracy of the recommendation. Besides, IGCEGA using the technique of global minima still resolves the problem associated with IGCN (Information Gain through Clustered Neighbor), which traps the algorithm in local clustering centroids. Although this problem was alleviated by IGCGA, IGCEGA solves the problem even better because IGCEGA assumes the lowest Mean Absolute Error (MAE), the evaluation matrix used in this work. Results of the experimentation of the various heuristics / techniques in RS used in personalization for cold start problems -- for instance Popularity, Entropy, IGCN, IGCGA - showed that IGCEGA is associated with the lowest MAE, therefore, best clustering, which in turn results into best recommendation.
2010 •
Genetic algorithms are becoming increasingly valuable in solving large-scale, realistic, difficult problems, and new customer personalization is one of these problems. In this paper, a method combining GA based clustering algorithm with Collaborative Filtering CF-based Recommender system is proposed named Information Gain Clustering using Genetic Algorithm (IGCGA), which alleviates the problem of being trapped in local clustering centroids using k-mean. Simulation results show that the proposed IGCGA, in most of the cases, is able to find much accurate personalization of new users compared to IGCN other Collaborative Filtering based Recommender system. Much better performance of IGCGA is observed.
International Journal of Computer Applications
A Hybrid Approach to Solve Cold Start Problem in Recommender Systems using Association Rules and Clustering Technique2013 •
The virtual world overflowing with the digital items which make the searching, choosing and shopping hard tasks for users. The recommender system is a smart filtering tool for generate a list of potential favorite items for the user to reduce the time needed by user to choose among a huge number of choices in websites and facilitate the process. In that context, this thesis presents a novel technique that combines the ideas of itembased semantic similarity, n-criteria and multi-filtering criteria with the genetic-based recommender system. The genetic algorithm is utilized in order to predict the best list of items to the active user. Consequently, each individual in the population represents a candidate recommendation list. Each list subjects to three tests to measure the quality of it. The proposed system alleviates the effect of the sparsity and cold start problems and makes the recommender system capable of generating recommendation without the need of using a similarity metric or requires any additional information provided by the hybrid system. Furthermore and due to the fact that there are many environments facing the information overload problem, the author presents a new classification of the recommender system based on the environment that is applied in. The proposed system is evaluated against the state-of-the-art genetic-based recommender system and the traditional techniques that used in collaborative filtering recommender system. The results obtained show that the proposed method outperforms these algorithms in prediction accuracy by 24.3%, recommendation quality by 33.5% and performance (CPU time) by 45.4%. Moreover, the results showed that 69.5% of the recommended items are truly favorite items to the active user. The remainders 30.5% of the recommended items are potential favorite items.
International Journal of Scientific & Technology Research
A State Of The Art Survey On Cold Start Problem In A Collaborative Filtering System2020 •
Internet is being flooded with information. Finding the necessary information is a difficult task. Recommender System is a panacea to this problem. Recommender System can help us finding a needle in a haystack. Recommender System takes a user- profile as an input and tries to find out products that shall be of interest to the user. Recommender System faces several challenges. One issue is the Cold- Start problem where a new product is not recommended to the user due to the unavailability of the necessary information about the product. In this paper, we survey the various solutions available to address the cold- start product when the recommender System uses a Collaborative Filtering based recommender systems. This study investigates how the cold-start problem is handled in the existing Recommender Systems and their application domains and also provides an analysis of various performance metrics.
International Journal of Advanced Computer Science and Applications
A Proposed Method to Solve Cold Start Problem using Fuzzy user-based Clustering2020 •
In the ecommerce services, there is a very important tool that will determine the success to increase number of buying and selling in the marketing target, that is how the user in finding products that are suitable to be purchased. This tool is called recommender system. Recommender system is important tool for establishing an effective communication between users and retailers in ecommerce business. Effective and enjoyable communication to find the product is considered to have a significant impact that increase of marketing achievement. Recommender system established in the mid-90s. Based on technical approach, there are four types of recommender system namely Collaborative, Contents Based, Knowledge Based and Demographic filtering. Collaborative filtering is considered to be more superior than another two methods. It offers obviously advantages in terms of serendipity, novelty and accuracy. Although it has some benefit. However, there is a critical problem in collaborative filtering that called cold start. It is a major problem to which many researchers have paid much more attention to this particular research interest. In response to this particular problem the critical review and analysis on state of the art of the current technology, some possible solutions including approach, method and techniques used have been identified but they need further validation.
Recommender systems have been used immensely commercially, scholastically and economically, recommendations created by these systems intend to offer relevant useful items to users. Several ways have been recommended for providing users with recommendations utilizing their rating history; most of these approaches suffer from new user problem (cold-start) which is the initial lack of items ratings. This paper suggest propose new user segment information to give suggestions as opposed to utilizing rating history to stay away cold-start problem. We present a framework for evaluating the usage of different segment qualities, such as age, gender, and occupation, for recommendation generation. Experiments are executed using Movie Lens dataset to evaluate the performance of the proposed framework. I. INTRODUCTION In the recent years, recommender system has been utilized colossally commercially, scholastically and economically for providing users with recommendation about products, services, or information which match their inclinations and interests. These suggestions are recommended by the system to guide user in a personalized way based on user's historical preferences to discover unseen items among an awesome gathering of items stored on the system. Recommender systems are utilized in various spaces to customize its applications by recommending items, such as books, movies, melodies, eateries, news articles, jokes, among others. Researchers have proposed several approaches for building recommender systems which offer items distinctively to users based in view of a particular supposition keeping in mind the end goal to coordinate their interests. By and by, all proposal approaches have qualities and shortcomings that ought to be considered while picking the most reasonable way to deal to implement. In this manner, hybrid recommenders are commonly utilized for joining at least two suggestion approaches together acquiring better execution and fewer drawbacks [1]. The recommendation systems types can be distinguished into two most used approaches: Collaborative Filtering: Collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating). It refers that users with comparative tastes will rate items likewise. It endeavors to discover users having comparable rating history to the target user (user who requires recommendations), building an area (neighborhood) from which the recommended items are generated. Content-based Filtering: This approach tries to recommend items that are similar to those that a user liked in the past (or is examining in the present). However, the above methodologies had been tended to suffer from new user problem, refers as cold-start problem, which is having initial lack of ratings when a new user join the system [3]. Since both methodologies supposition are based upon user's ratings history, this issue can significantly influence adversely the recommender performance because of the inability of the system to deliver significant suggestions [4]. Hence, an option sort of input (alternative) is required to be acquired explicitly from users to be utilized for suggesting recommendations instead of ratings.
The aim of recommendation systems is to provide users with items that they may be interested in. However, one of the most serious issues for systems to recommend is a problem known as cold start, which happens when new users or items are introduced to the system with no previous knowledge of them. There are many proposals in the literature that aim to deal with this issue. In some cases the user is required to provide some explicit information about them, which demands some effort on their part. In this paper we will introduce how communication information will be used to create a behavioral profile to differentiate users and based on this section will create predictions using machine learning methods. This paper conducts a systematic analysis of the literature to assess the use of machine learning techniques in recommendation systems and to identify areas for further study. The overall survey of this paper will address the research gap and opportunities with the Recommendations system(RS).
International Journal of Electrical and Computer Engineering (IJECE)
An advanced approach for accurate pneumonia detection using combined deep convolutional neural networksEnfermería Nefrológica
Rueda alimentaria para pacientes con insuficiencia renal crónica en hemodiálisis: recomendaciones dietéticas fundamentales2013 •
Zenodo (CERN European Organization for Nuclear Research)
Détermination empirique de la taille fiscale au Maroc2023 •
TUGAS HUKUM & HAM
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A respiratory health survey of a subsurface smoldering landfill2018 •
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