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How do item features and user characteristics affect users’ perceptions of recommendation serendipity? A cross-domain analysis

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Abstract

Serendipity is one of beyond-accuracy objectives for recommender systems (RSs), which aims to achieve both relevance and unexpectedness of recommendations, so as to potentially address the “filter bubble” issue of traditional accuracy-oriented RSs. However, so far most of the serendipity-oriented studies have focused on developing algorithms to consider various types of item features or user characteristics, but are largely based on their own assumptions. Few have stood from users’ perspective to identify the effects of these features on users’ perceptions of the serendipity of the recommendation. Therefore, in this paper, we have analyzed their effects with two user survey datasets. These are the Movielens Serendipity Dataset of 467 users’ responses to a retrospective survey of their perceptions of the recommended movie’s serendipity, and the Taobao Serendipity Dataset of 11,383 users’ perceptions of the serendipity of a recommendation received at a mobile e-commerce platform. In both datasets, we have analyzed the correlations between users’ serendipity perceptions and various types of item features (i.e., item-driven such as popularity, profile-driven such as in-profile diversity, and interaction-driven including category-level and item-level features), as well as the influence of several user characteristics (including the Big-Five personality traits and curiosity). The results disclose both domain-independent and domain-specific observations, which may be constructive in enhancing current serendipity-oriented recommender systems by better utilizing item features and user data.

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Notes

  1. https://grouplens.org/datasets/serendipity-2018/.

  2. https://github.com/greenblue96/Taobao-Serendipity-Dataset.

  3. In the Movielens dataset, there are 19 movie categories (genres) such as “Drama,” “Fantasy,” “Romance,” “Comedy,” etc.

  4. We also computed variations using the top-level categories. Since the results are similar, we have only reported the results of using the leaf-level categories.

  5. 83 (user, item) pairs did not contain answers to all the three unexpectedness questions, so they were excluded from the analysis.

  6. The value indicates that, for example, if popularity rises by one unit, users’ perceived serendipity will decrease by \(|1-{0.821}|*100\%={17.9}\%\), while one-unit increase of day-of-the-week interaction will lead to \(|1-{1.135}|*100\%={13.5}\%\) increase of users’ perceived serendipity.

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Acknowledgements

This work was supported by Hong Kong Research Grants Council (RGC) (project RGC/HKBU12201620). We are also thankful for Yonghua Yang, Keping Yang, and Quan Yuan who helped collect the data in the previous work (Chen et al. 2019b). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the collaborators and sponsor.

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    Ningxia Wang & Li Chen

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Wang, N., Chen, L. How do item features and user characteristics affect users’ perceptions of recommendation serendipity? A cross-domain analysis. User Model User-Adap Inter 33, 727–765 (2023). https://doi.org/10.1007/s11257-022-09350-x

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