本文主要研究User fatigue (包括same item fatigue和same category fatigue)(用户的兴趣随着物品/类别重复显示次数的增加而改变) 对推荐列表被用户点击概率的影响(CTR)。
基于历史数据的统计信息,文章研究了多个与User Fatigue有关的因素,并得出了一些的结论:
启示与收获:
文献题目 | 去谷歌学术搜索 | ||||||||||
User Fatigue in Online News Recommendation | |||||||||||
文献作者 | Hao Ma; Xueqing Liu; Zhihong Shen | ||||||||||
文献发表年限 | 2016 | ||||||||||
文献关键字 | |||||||||||
Recommender Systems, User Fatigue, News Recommendation, Click Prediction, User Modeling | |||||||||||
摘要描述 | |||||||||||
Many aspects and properties of Recommender Systems have been well studied in the past decade, however, the impact of User Fatigue has been mostly ignored in the literature. User fatigue represents the phenomenon that a user quickly loses the interest on the recommended item if the same item has been presented to this user multiple times before. The direct impact caused by the user fatigue is the dramatic decrease of the Click Through Rate (CTR, i.e., the ratio of clicks to impressions). In this paper, we present a comprehensive study on the research of the user fatigue in online recommender systems. By analyzing user behavioral logs from Bing Now news recommendation, we find that user fatigue is a severe problem that greatly affects the user experience. We also notice that different users engage differently with repeated recommendations. Depending on the previous users’ interaction with repeated recommendations, we illustrate that under certain condition the previously seen items should be demoted, while some other times they should be promoted. We demonstrate how statistics about the analysis of the user fatigue can be incorporated into ranking algorithms for personalized recommendations. Our experimental results indicate that significant gains can be achieved by introducing features that reflect users’ interaction with previously seen recommendations (up to 15% enhancement on all users and 34% improvement on heavy users). |