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

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Human Interface and the Management of Information. Interacting with Information (HCII 2020)

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Abstract

The term “serendipity” has been understood narrowly in the Recommender System. Applying a user-centered approach, user-friendly serendipitous recommender systems are expected to be developed based on a good understanding of serendipity. In this paper, we introduce CHESTNUT, a memory-based movie collaborative filtering system to improve serendipity performance. Relying on a proposed Information Theory-based algorithm and previous study, we demonstrate a method of successfully injecting insight, unexpectedness and usefulness, which are key metrics for a more comprehensive understanding of serendipity, into a practical serendipitous recommender system. With lightweight experiments, we have revealed a few runtime issues and further optimized the same. We have evaluated CHESTNUT in both practicability and effectiveness, and the results show that it is fast, scalable and improves serendipity performance significantly, compared with mainstream memory-based collaborative filtering. The source codes of CHESTNUT are online at https://github.com/unnc-ucc/CHESTNUT.

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Notes

  1. 1.

    Those users denoted as target users.

  2. 2.

    Attribute(s) to guide making connections.

  3. 3.

    Those users denoted as active users.

  4. 4.

    In movie recommendations, for instance, it could be directors, genres and so on.

  5. 5.

    For example, Pearson Correlation Similarity, and so on.

  6. 6.

    More specifically, their items.

  7. 7.

    Those items from active users, generated by the target user.

  8. 8.

    Information with regard to the referencing attribute.

  9. 9.

    In this rating scale, the full mark is 5.0.

  10. 10.

    Here, the similarity refers to Pearson-Correlation Similarity.

  11. 11.

    When the value is less than it, making connections terminates.

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Acknowledgement

We thank for valuable feedback and suggestions from our group members and anonymous reviewers, which have substantially improved the overall quality of this paper. This research is generously supported by National Natural Science Foundation of China Grant No. 71301085 and Hefeng Creative Industrial Park in Ningbo, China.

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Authors and Affiliations

  1. User-Centric Computing Group, University of Nottingham Ningbo China, Ningbo, China

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

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  1. Xiangjun Peng
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  2. Hongzhi Zhang
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  3. Xiaosong Zhou
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  4. Shuolei Wang
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Correspondence to Xu Sun .

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Editors and Affiliations

  1. Tokyo University of Science, Tokyo, Japan

    Sakae Yamamoto

  2. Tokyo City University, Tokyo, Japan

    Hirohiko Mori

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Peng, X., Zhang, H., Zhou, X., Wang, S., Sun, X., Wang, Q. (2020). CHESTNUT: Improve Serendipity in Movie Recommendation by an Information Theory-Based Collaborative Filtering Approach. In: Yamamoto, S., Mori, H. (eds) Human Interface and the Management of Information. Interacting with Information. HCII 2020. Lecture Notes in Computer Science(), vol 12185. Springer, Cham. https://doi.org/10.1007/978-3-030-50017-7_6

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  • DOI: https://doi.org/10.1007/978-3-030-50017-7_6

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