Abstract
With the development of e-commerce, e-commerce websites become very popular. People write reviews on products and rate the helpfulness of reviews in these websites. Reviews written by a user and reviews rated by a user actually reflect a user’s interests and disinterest. Thus, they are very useful for user profiling. In this paper, we explore users’ reviews and ratings on reviews for personalized search and propose a review-based user profiling method. And we also propose a prioritybased result ranking strategy. For evaluation, we conduct experiments on a real-life data set. The experimental results show that our method can significantly improve the retrieval quality.
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CITATION STYLE
Yang, Y., Hu, S., Cai, Y., Du, Q., Leung, H. F., & Lau, R. Y. K. (2016). Exploring reviews and ratings on reviews for personalized search. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9584 LNCS, pp. 140–150). Springer Verlag. https://doi.org/10.1007/978-3-319-32865-2_16
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