Recommender system (RS) can produce personalized service to users by analyzing their historical information. User-based collaborative filtering (UBCF) approach is widely utilized in practical RSs because of its excellent performance. However, the traditional UBCF suffers from several inherent problems, such as data sparsity and new user cold start. In this paper, we propose a novel approach, namely covering reduction collaborative filtering, to solve data sparsity and new user cold start problems in UBCF. First, we define the redundant users in a new user’s neighborhood through a detailed analysis on two real-world datasets (i.e., MovieLens and Netflix). Then, we analyze the intrinsic connection between redundant users in UBCF and redundant elements in covering-based rough sets, and transform the redundant user removal issue into the redundant element reduction. Furthermore, a cover is built for each new user according to the information of candidate neighbors. And the covering reduction algorithm is employed to remove the redundant elements in the cover of each new user, removing all reducible elements in a cover means redundant users in the neighborhood of a new user are removed. Finally, rating scores for unrated items are predicted by aggregating the ratings of remaining users after reduction. And items with the highest predicted rating scores will be recommended to the new user. Experimental results suggest that for the sparse datasets that often occur in real RSs, the proposed approach outperforms those of existing work and can provide recommendations for a new user with satisfactory accuracy and diversity simultaneously without requiring any other special additional information.

中文翻译：

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在基于用户的协作过滤中使用邻域缩减来缓解稀疏性和冷启动问题

推荐系统（RS）可以通过分析用户的历史信息为用户提供个性化服务。基于用户的协作过滤（UBCF）方法由于其出色的性能而在实际的RS中得到了广泛使用。但是，传统的UBCF存在一些固有的问题，例如数据稀疏性和新用户冷启动。在本文中，我们提出了一种新颖的方法，即涵盖归约协作过滤，以解决UBCF中的数据稀疏性和新用户冷启动问题。首先，我们通过对两个真实世界数据集（即MovieLens和Netflix）的详细分析来定义新用户附近的冗余用户。然后，我们分析UBCF中的冗余用户与基于覆盖的粗糙集中的冗余元素之间的内在联系，并将多余的用户删除问题转化为多余的元素减少。此外，根据候选邻居的信息为每个新用户建立封面。并且采用覆盖减少算法来移除每个新用户的封面中的冗余元素，移除封面中的所有可约简元素意味着移除新用户附近的冗余用户。最后，通过汇总减少后剩余用户的评分来预测未评分项目的评分。预测评分最高的项目将推荐给新用户。实验结果表明，对于在真实RS中经常出现的稀疏数据集，