TY - GEN
T1 - User fatigue in online news recommendation
AU - Ma, Hao
AU - Liu, Xueqing
AU - Shen, Zhihong
PY - 2016
Y1 - 2016
N2 - 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).
AB - 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).
KW - Click Prediction
KW - News Recommendation
KW - Recommender Systems
KW - User Fatigue
KW - User Modeling
UR - http://www.scopus.com/inward/record.url?scp=85008190136&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85008190136&partnerID=8YFLogxK
U2 - 10.1145/2872427.2874813
DO - 10.1145/2872427.2874813
M3 - Conference contribution
AN - SCOPUS:85008190136
T3 - 25th International World Wide Web Conference, WWW 2016
SP - 1363
EP - 1372
BT - 25th International World Wide Web Conference, WWW 2016
T2 - 25th International World Wide Web Conference, WWW 2016
Y2 - 11 April 2016 through 15 April 2016
ER -