Enhancing Collaborative Filtering by User Interest Expansion via Personalized Ranking
Enhancing Collaborative Filtering by User Interest Expansion via Personalized Ranking
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DOI：
10.1109/tsmcb.2011.2163711
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发表时间：
2012-02-01
影响因子：
--
通讯作者：
Chen, Jian
中科院分区：
文献类型：
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作者：
Liu, Qi;Chen, Enhong;Chen, Jian
推荐系统通过理解用户过去的行为，从许多可能的选择中向用户推荐几个项目。在这些系统中，用户的行为受到用户隐藏兴趣的影响。学会利用有关用户兴趣的信息对于提出更好的推荐通常是至关重要的。然而，现有的基于协同过滤的推荐系统通常专注于挖掘用户与系统交互的信息，而对潜在用户兴趣信息的挖掘却很少。为此，受主题模型的启发，本文提出了一种基于协同过滤的推荐系统iExpand，该系统通过个性化排名扩展用户兴趣。其目标是建立一个面向项目、基于模型的协作过滤框架。IExpand方法引入用户-兴趣-项三层表示方案，以较少的计算代价获得更准确的排序结果，并有助于理解用户、项和用户兴趣之间的交互关系。此外，iExpand战略性地处理了传统协作过滤方法中存在的许多问题，如过度专业化问题和冷启动问题。最后，我们在三个基准数据集上对iExpand进行了评估，实验结果表明，iExpand的排名性能比现有的排名方法要好得多。
Recommender systems suggest a few items from many possible choices to the users by understanding their past behaviors. In these systems, the user behaviors are influenced by the hidden interests of the users. Learning to leverage the information about user interests is often critical for making better recommendations. However, existing collaborative-filtering-based recommender systems are usually focused on exploiting the information about the user's interaction with the systems; the information about latent user interests is largely underexplored. To that end, inspired by the topic models, in this paper, we propose a novel collaborative-filtering-based recommender system by user interest expansion via personalized ranking, named iExpand. The goal is to build an item-oriented model-based collaborative-filtering framework. The iExpand method introduces a threelayer, user-interests-item, representation scheme, which leads to more accurate ranking recommendation results with less computation cost and helps the understanding of the interactions among users, items, and user interests. Moreover, iExpand strategically deals with many issues that exist in traditional collaborative-filtering approaches, such as the overspecialization problem and the cold-start problem. Finally, we evaluate iExpand on three benchmark data sets, and experimental results show that iExpand can lead to better ranking performance than state-of-the-art-methods with a significant margin.