Abstract
Many mechanisms have been developed to deliver only relevant information to the web users and prevent information overload. The most popular recent developments in the e-commerce domain are the user-preference based personalization and recommendation techniques. However, the existing techniques have a major drawback – poor accuracy of recommendation on one-and-only items – because most of them do not understand the item’s semantic features and attributes. Thus, in this study, we propose a novel Semantic Product Relevance model and its attendant personalized recommendation approach to assist Export business selecting the right international trade exhibitions for market promotion. A recommender system, called Smart Trade Exhibition Finder (STEF), is developed to tailor the relevant trade exhibition information to each particular business user. STEF reduces significantly the time, cost and risk faced by exporters in selecting, entering and developing international markets. In particular, the proposed model can be used to overcome the drawback of existing recommendation techniques.
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Guo, X., Zhang, G., Chew, E., Burdon, S. (2005). A Hybrid Recommendation Approach for One-and-Only Items. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_48
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DOI: https://doi.org/10.1007/11589990_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30462-3
Online ISBN: 978-3-540-31652-7
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