CHESTNUT: Improve serendipity in movie recommendation by an Information Theory-based collaborative filtering approach

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

Research output: Working paper

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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 runtime 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 serendip-ity performance significantly, compared with mainstream memory-based collaborative filtering. The source codes of CHESTNUT are online at https://github.com/unnc-idl-ucc/CHESTNUT/.
Original languageEnglish
PublisherUnpublished
Publication statusPublished - 1 Jan 2019

Publication series

Name
PublisherUnpublished

Keywords

  • InformationTheory
  • RecommederSystems
  • Serendipity

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