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Recommending Based on Implicit Feedback

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Social Information Access

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10100))

Abstract

Recommender systems have shown to be valuable tools for filtering, ranking, and discovery in a variety of application domains such as e-commerce, media repositories or document-based information in general that includes the various scenarios of Social Information Access discussed in this book. One key to the success of such systems lies in the precise acquisition or estimation of the user’s preferences. While general recommender systems research often relies on the existence of explicit preference statements for personalization, such information is often very sparse or unavailable in real-world applications. Information that allows us to assess the relevance of certain items indirectly through a user’s actions and behavior (implicit feedback) is in contrast often available in abundance. In this chapter we categorize different types of implicit feedback and review their use in the context of recommender systems and Social Information Access applications. We then extend the categorization scheme to be suitable to recent application domains. Finally, we present state-of-the-art algorithmic approaches, discuss challenges when using implicit feedback signals in particular with respect to popularity biases, and discuss selected recent works from the literature.

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Notes

  1. 1.

    http://finance.yahoo.com/news/netflix-wants-ditch-5-star-202428660.html.

  2. 2.

    http://recsys.acm.org/recsys15/challenge/.

  3. 3.

    As indicated in Sect. 2, we consider such information only as implicit feedback if the signal is related to some target recommendation object.

  4. 4.

    http://michael.hahsler.net/research/association_rules/measures.html.

  5. 5.

    The BPR-OPT criterion used in the previously described BPR method has a close correspondence to the AUC measure.

  6. 6.

    This was for example done for the evaluation of the implicit-only algorithm BPR, see http://www.mymedialite.net/examples/item_recommendation_datasets.html.

  7. 7.

    Many more of the top-ranked elements might be relevant for the user, but no explicit information is given.

  8. 8.

    The data is not publicly available.

  9. 9.

    The data was sampled in a way that no conclusions about visitor or sales numbers can be drawn.

  10. 10.

    The data sample was taken within a very limited period of time.

  11. 11.

    The importance of feature-based similarities was also the basis in [119].

  12. 12.

    http://labrosa.ee.columbia.edu/millionsong/challenge.

  13. 13.

    http://www.weibo.com.

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Authors and Affiliations

  1. AAU Klagenfurt, 9020, Klagenfurt, Austria

    Dietmar Jannach

  2. Department of Computer Science, TU Dortmund University, 44221, Dortmund, Germany

    Lukas Lerche

  3. Free University of Bozen-Bolzano, 39100, Bozen, Italy

    Markus Zanker

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  1. Dietmar Jannach
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  1. University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Peter Brusilovsky

  2. University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Daqing He

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Jannach, D., Lerche, L., Zanker, M. (2018). Recommending Based on Implicit Feedback. In: Brusilovsky, P., He, D. (eds) Social Information Access. Lecture Notes in Computer Science(), vol 10100. Springer, Cham. https://doi.org/10.1007/978-3-319-90092-6_14

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