Proceedings ArticleDOI
User Fatigue in Online News Recommendation
Hao Ma,Xueqing Liu,Zhihong Shen +2 more
- pp 1363-1372
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TLDR
By analyzing user behavioral logs from Bing Now news recommendation, it is found that user fatigue is a severe problem that greatly affects the user experience and experimental results indicate that significant gains can be achieved by introducing features that reflect users' interaction with previously seen recommendations.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).read more
Citations
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Journal ArticleDOI
News recommender systems – Survey and roads ahead
TL;DR: This work reviews the state-of-the-art of designing and evaluating news recommender systems over the last ten years and analyzes which particular challenges of news recommendation have been well explored and which areas still require more work.
Journal ArticleDOI
Contextual Hybrid Session-Based News Recommendation With Recurrent Neural Networks
TL;DR: In this paper, a contextual hybrid, deep learning based approach for session-based news recommendation is presented, which is able to leverage a variety of information types, including the user's short-term reading interests, the reader's context, or the recency or popularity of an article.
Journal ArticleDOI
News recommender system: a review of recent progress, challenges, and opportunities
Shaina Raza,Chen Ding +1 more
TL;DR: In this paper, a survey of the state-of-the-art news recommender systems (NRS) is presented, which highlights the major challenges faced by the NRS and identifies the possible solutions from the state of the art.
Journal ArticleDOI
Exploiting search history of users for news personalization
TL;DR: This paper proposes a novel approach that relies on the concept of search profiles, which are user profiles that are built based on the past interactions of the user with a web search engine, and extensively test the proposal on real-world datasets obtained from Yahoo.
Journal ArticleDOI
A Review of Text-Based Recommendation Systems
TL;DR: Text-based recommendation systems (RS) as discussed by the authors are the systems with the capability to find the relevant information in a minimal time using text as the primary feature and there exist several techniques to build and evaluate such systems.
References
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Journal ArticleDOI
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TL;DR: As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.
Proceedings ArticleDOI
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Proceedings ArticleDOI
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Journal ArticleDOI
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Proceedings ArticleDOI
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