Quantitative analysis of Matthew effect and sparsity problem of recommender systems
Quantitative analysis of Matthew effect and sparsity problem of recommender systems
复制标题
DOI：
10.1109/icccbda.2018.8386490
复制
发表时间：
2018-04
期刊：
影响因子：
--
通讯作者：
Hao Wang;Zonghu Wang;Weishi Zhang
中科院分区：
文献类型：
--
作者：
Hao Wang;Zonghu Wang;Weishi Zhang
推荐系统已经获得了巨大的商业成功。推荐已经在电子商务、在线音乐调频、在线新闻门户等领域得到了广泛的应用。然而，与输入数据结构相关的几个问题给推荐系统的性能带来了严重的挑战。其中两个问题是马太效应和稀疏性问题。马修效应严重地将推荐系统的输出向热门商品倾斜。数据稀疏性问题直接影响推荐结果的覆盖率。协同过滤是业界普遍采用的一个简单的基准，作为推荐系统设计的基线。了解协作过滤的基本机制对于进一步优化至关重要。在本文中，我们对协同过滤特定语境下的马太效应和稀疏性问题进行了深入的定量分析。我们比较了基于用户的协同过滤和基于项目的协同过滤的基本机制，并对行业推荐系统的构建人员提供了见解。
Recommender systems have received great commercial success. Recommendation has been widely used in areas such as e-commerce, online music FM, online news portal, etc. However, several problems related to input data structure pose serious challenge to recommender system performance. Two of these problems are Matthew effect and sparsity problem. Matthew effect heavily skews recommender system output towards popular items. Data sparsity problem directly affects the coverage of recommendation result. Collaborative filtering is a simple benchmark ubiquitously adopted in the industry as the baseline for recommender system design. Understanding the underlying mechanism of collaborative filtering is crucial for further optimization. In this paper, we do a thorough quantitative analysis on Matthew Effect and sparsity problem in the particular context setting of collaborative filtering. We compare the underlying mechanism of user-based and item-based collaborative filtering and give insight to industrial recommender system builders.