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Considering temporal aspects in recommender systems: a survey

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Abstract

The widespread use of temporal aspects in user modeling indicates their importance, and their consideration showed to be highly effective in various domains related to user modeling, especially in recommender systems. Still, past and ongoing research, spread over several decades, provided multiple ad-hoc solutions, but no common understanding of the issue. There is no standardization and there is often little commonality in considering temporal aspects in different applications. This may ultimately lead to the problem that application developers define ad-hoc solutions for their problems at hand, sometimes missing or neglecting aspects that proved to be effective in similar cases. Therefore, a comprehensive survey of the consideration of temporal aspects in recommender systems is required. In this work, we provide an overview of various time-related aspects, categorize existing research, present a temporal abstraction and point to gaps that require future research. We anticipate this survey will become a reference point for researchers and practitioners alike when considering the potential application of temporal aspects in their personalized applications.

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Notes

  1. Gama et al. (2014) defined concept drift as changes in the conditional distribution of the output given the input.

  2. https://recsys.acm.org/.

  3. We share the raw data that was collected in this process online: https://github.com/sveron/TempAspectsSurvey.

  4. Such a window may for example express that items that were most popular in the last n days should be considered to be particularly relevant.

  5. Complementary purchasing is the purchasing of items that are consumed in combination with this specific item and add some value one to another (cereal and milk)

  6. See Quadrana et al. (2018) for a categorization of sequence-aware recommendation models into session-based and session-aware ones.

  7. https://grouplens.org/datasets/movielens/20m/.

  8. https://github.com/hexiangnan/sigir16-eals.

  9. https://sites.google.com/site/xueatalphabeta/academic-projects.

  10. http://millionsongdataset.com/tasteprofile/.

  11. http://www.bigdatalab.ac.cn/benchmark/bm/dd?data=Ta-Feng.

  12. https://tianchi.aliyun.com/dataset/dataDetail?dataId=649 &userId=1.

  13. https://www.aicrowd.com/challenges/spotify-million-playlist-dataset-challenge.

  14. https://cikm2016.cs.iupui.edu/cikm-cup/.

  15. Roughly speaking, in what Steck named “calibrated recommendations” in (Steck 2018), the distribution of item properties in the recommendation list correlates with the distribution of the items in the user profile. See also Jugovac et al. (2017) for an analysis of earlier related methods.

  16. https://gdpr-info.eu/.

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Acknowledgements

This study was partially supported by the Israeli Science Foundation (ISF) Grant Number 262/2017. This work was also partly supported by industry partners and the Research Council of Norway with funding to MediaFutures: Research Centre for Responsible Media Technology and Innovation, through the centers for Research-based Innovation scheme, Project Number 309339.

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  1. University of Haifa, Haifa, Israel

    Veronika Bogina & Tsvi Kuflik

  2. University of Klagenfurt, Klagenfurt, Austria

    Dietmar Jannach

  3. Kempelen Institute of Intelligent Technologies, Bratislava, Slovakia

    Maria Bielikova & Michal Kompan

  4. University of Bergen, Bergen, Norway

    Christoph Trattner

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  1. Veronika Bogina
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Bogina, V., Kuflik, T., Jannach, D. et al. Considering temporal aspects in recommender systems: a survey. User Model User-Adap Inter 33, 81–119 (2023). https://doi.org/10.1007/s11257-022-09335-w

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