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Social tagging in Recommender Systems

机译:推荐系统中的社交标记

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The Web 2.0 gave the Internet users a virtual life, in which they could shop online and try to socialize through Web. Recommender Systems (RS) improves users' shopping experience by by recommending them a shopping item. Many techniques have been introduced to enhance RS algorithms, including social tagging. Social Tagging let users share resources and this lead to more personalized recommendation. There is a lack of overall information about RS algorithms that have implemented social tagging. Therefore, in this paper we compared and analysed some of the studies that have particularly used social tagging in recommender systems. Both Collaborative Filtering (CF) and Content-based filtering systems were compared, and results show that it is better to combine these algorithms for achieving higher personalized recommendation, and also to address the cold start issue.
机译:Web 2.0给了互联网用户一个虚拟生活,他们可以在线购物并尝试通过网络进行社交。 推荐系统(RS)通过推荐他们的购物体验来提高用户购物体验。 已经引入了许多技术来增强RS算法,包括社交标记。 社交标记让用户共享资源,这导致更加个性化的推荐。 缺乏有关已实现社交标记的RS算法的整体信息。 因此,在本文中,我们比较并分析了一些在推荐系统中特别使用的社交标记的研究。 比较了协作滤波(CF)和基于内容的滤波系统,结果表明,最好将这些算法结合起来实现更高的个性化推荐,以及解决冷启动问题。

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