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首页> 外文期刊>Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on >Enhancing Collaborative Filtering by User Interest Expansion via Personalized Ranking
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Enhancing Collaborative Filtering by User Interest Expansion via Personalized Ranking

机译：通过个性化排名通过用户兴趣扩展来增强协作过滤

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摘要

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 three-layer, 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.
机译：推荐系统通过了解用户的过去行为，向用户建议许多可能的选择。在这些系统中，用户行为受到用户隐藏利益的影响。学习利用有关用户兴趣的信息通常对于提出更好的建议至关重要。但是，现有的基于协作过滤的推荐系统通常集中于利用有关用户与系统交互的信息。有关潜在用户兴趣的信息在很大程度上尚未得到开发。为此，在主题模型的启发下，我们提出了一种通过基于个性化排名的用户兴趣扩展来基于协作过滤的新型推荐系统，名为iExpand。目标是建立一个基于项目的基于模型的协作过滤框架。 iExpand方法引入了一个三层的用户－兴趣－项表示方案，该方案可以以更低的计算成本获得更准确的排名推荐结果，并有助于理解用户，项目和用户兴趣之间的交互。此外，iExpand从战略上解决了传统协作过滤方法中存在的许多问题，例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评估了iExpand，实验结果表明，与具有显着优势的最新方法相比，iExpand可以带来更好的排名性能。

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