Integrating User Reviews and Ratings for Enhanced Personalized Searching

Shuyue Hu, Y. Cai, Ho-fung Leung, Dongping Huang, Yang Yang
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引用次数: 1

Abstract

With the development of e-commerce, websites such as Amazon and eBay have become very popular. Users post reviews of products and rate the helpfulness of reviews on these websites. Reviews written by a user and reviews rated by a user reflect the user's interests and disinterest. Thus, they are very useful for user profiling. In this study, the authors explore users' reviews and ratings of reviews for personalized searching and propose a review-based user profiling method. To satisfy a user's basic information needs, expressed in the form of a query, they also propose a priority-based result ranking strategy. For evaluation, they conduct experiments on a real-life data set. The experimental results show that their method can significantly improve retrieval quality.
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整合用户评论和评级,增强个性化搜索
随着电子商务的发展,像亚马逊和eBay这样的网站变得非常受欢迎。用户在这些网站上发布产品评论,并对评论的有用性进行评级。由用户撰写的评论和由用户评分的评论反映了用户的兴趣和不兴趣。因此,它们对于用户分析非常有用。在本研究中,作者探索了用户评论和评论评分用于个性化搜索,并提出了一种基于评论的用户分析方法。为了满足用户以查询形式表达的基本信息需求,他们还提出了基于优先级的结果排序策略。为了评估,他们在真实的数据集上进行实验。实验结果表明,该方法能显著提高检索质量。
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