CHESTNUT: Improve Serendipity in Movie Recommendation by an Information Theory-Based Collaborative Filtering Approach

Peng, Xiangjun; Zhang, Hongzhi; Zhou, Xiaosong; Wang, Shuolei; Sun, Xu; Wang, Qingfeng

doi:10.1007/978-3-030-50017-7_6

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…Although the theoretical support of CHESTNUT [24], its effectiveness [23] and practical system performance [16] has been examined earlier, the missing validation from real-world users is still missing. Also, a large-scale user study would also help to uncover several issues, which are not capable to be found through off-line evaluations, and enhance its practicality.…”

Section: Background and Motivationmentioning

confidence: 99%

“…Towards a more comprehensive understanding of serendipity, we have built CHESTNUT , the first serendipitous movie recommender system with an Information Theory-based algorithm, to embed a more comprehensive understanding of serendipity in a practical recommender system [16,24]. Although experimental studies on static data sets have shown that CHESTNUT could achieve significant improvements (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Early Findings from a Large-Scale User Study of CHESTNUT: Validations and Implications

Peng

Huang

Yang

et al. 2020

Lecture Notes in Computer Science

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Towards a serendipitous recommender system with user-centred understanding, we have built CHESTNUT , an Information Theorybased Movie Recommender System, which introduced a more comprehensive understanding of the concept. Although off-line evaluations have already demonstrated that CHESTNUT has greatly improved serendipity performance, feedback on CHESTNUT from real-world users through online services are still unclear now. In order to evaluate how serendipitous results could be delivered by CHESTNUT , we consequently designed, organized and conducted large-scale user study, which involved 104 participants from 10 campuses in 3 countries. Our preliminary feedback has shown that, compared with mainstream collaborative filtering techniques, though CHESTNUT limited users' feelings of unexpectedness to some extent, it showed significant improvement in their feelings about certain metrics being both beneficial and interesting, which substantially increased users' experience of serendipity. Based on them, we have summarized three key takeaways, which would be beneficial for further designs and engineering of serendipitous recommender systems, from our perspective.

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Section: Background and Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%