Abstract
Towards a serendipitous recommender system with user-centred understanding, we have built CHESTNUT, an Information Theory-based 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. All details of our large-scale user study could be found at https://github.com/unnc-idl-ucc/Early-Lessons-From-CHESTNUT.
Highlights
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]
Experimental studies on static data sets have shown that CHESTNUT could achieve significant improvements, compared with other mainstream collaborative filtering approaches, in the incidence of serendipity, it remains necessary for a user study to be conducted to allow validations of CHESTNUT and impose further investigations into the concept of serendipity and the engineering of serendipitous recommender systems
We have presented our early findings from a large-scale user study of CHESTNUT, which involved 104 participants over a ten months period
Summary
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]. Experimental studies on static data sets have shown that CHESTNUT could achieve significant improvements (i.e. around 2.5x), compared with other mainstream collaborative filtering approaches, in the incidence of serendipity, it remains necessary for a user study to be conducted to allow validations of CHESTNUT and impose further investigations into the concept of serendipity and the engineering of serendipitous recommender systems. Serendipity has been understood narrowly within the Recommender System field, and it has been defined in previous research as receiving an unexpected and fortuitous item recommendation [13] Such mindset have led to many efforts in the development and investigation of serendipitous recommender systems through modelling and algorithmic designs and optimizations, instead of rethinking the natural understanding of the concept. We have performed a large-scale user study since we believe such a study is essential, important and meaningful for both CHESTNUT -related work and the communities of Recommender Systems and Information Management
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