User fatigue in online news recommendation

Hao Ma, Xueqing Liu, Zhihong Shen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

29 Scopus citations

Abstract

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).

Original languageEnglish
Title of host publication25th International World Wide Web Conference, WWW 2016
Pages1363-1372
Number of pages10
ISBN (Electronic)9781450341431
DOIs
StatePublished - 2016
Event25th International World Wide Web Conference, WWW 2016 - Montreal, Canada
Duration: 11 Apr 201615 Apr 2016

Publication series

Name25th International World Wide Web Conference, WWW 2016

Conference

Conference25th International World Wide Web Conference, WWW 2016
Country/TerritoryCanada
CityMontreal
Period11/04/1615/04/16

Keywords

  • Click Prediction
  • News Recommendation
  • Recommender Systems
  • User Fatigue
  • User Modeling

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