论文标题
基于协作过滤的多媒体推荐系统中的受欢迎程度偏见
Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems
论文作者
论文摘要
多媒体推荐系统建议通过利用传统推荐系统（例如协作过滤）的概念来建议用户，例如歌曲，（数字）书籍和电影。在本文中，我们研究了这种基于协作过滤的多媒体推荐系统的潜在问题，即流行偏见，导致建议列表中不受欢迎的项目的代表性不足。因此，我们研究了四个多媒体数据集，即LastFM，Movielens，BookCrossing和缅甸主义者，我们每个人都分为三个用户群体，它们在受欢迎程度上有所不同，即Lowpop，MedPop和HighPop。使用这些用户组，我们评估了四个基于协作过滤的算法，就项目和用户级别的受欢迎程度偏差而言。我们的发现是三个方面：首先，我们表明对流行项目的兴趣很少的用户往往具有较大的用户资料，因此是多媒体推荐系统的重要数据源。其次，我们发现，推荐比不受欢迎的物品更频繁地推荐了流行物品。第三，我们发现对受欢迎物品的兴趣很少的用户获得的建议比中等或高兴趣的用户更加糟糕。
Multimedia recommender systems suggest media items, e.g., songs, (digital) books and movies, to users by utilizing concepts of traditional recommender systems such as collaborative filtering. In this paper, we investigate a potential issue of such collaborative-filtering based multimedia recommender systems, namely popularity bias that leads to the underrepresentation of unpopular items in the recommendation lists. Therefore, we study four multimedia datasets, i.e., LastFm, MovieLens, BookCrossing and MyAnimeList, that we each split into three user groups differing in their inclination to popularity, i.e., LowPop, MedPop and HighPop. Using these user groups, we evaluate four collaborative filtering-based algorithms with respect to popularity bias on the item and the user level. Our findings are three-fold: firstly, we show that users with little interest into popular items tend to have large user profiles and thus, are important data sources for multimedia recommender systems. Secondly, we find that popular items are recommended more frequently than unpopular ones. Thirdly, we find that users with little interest into popular items receive significantly worse recommendations than users with medium or high interest into popularity.
