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Item Recommendation from Implicit Feedback

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Recommender Systems Handbook

Abstract

The task of item recommendation is to select the best items for a user from a large catalogue of items. Item recommenders are commonly trained from implicit feedback which consists of past actions that are positive only. Core challenges of item recommendation are (1) how to formulate a training objective from implicit feedback and (2) how to efficiently train models over a large item catalogue. This chapter formulates the item recommendation problem and points out its unique characteristics. Then different training objectives are discussed. The main body deals with learning algorithms and presents sampling based algorithms for general recommenders and more efficient algorithms for dot product models. Finally, the application of item recommenders for retrieval tasks is discussed.

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Notes

  1. 1.

    To be precise, \(\hat {y}\) should be \(\hat {y}_{\boldsymbol {\theta }}\), but for convenience, the subscript is omitted in this chapter.

  2. 2.

    The analysis here ignores the cost for computing the embeddings ϕ(c) and ψ(i). The derived results have a linear complexity in the costs for computing the embeddings.

  3. 3.

    [13] uses a different weight on each Gramian element which makes the derivation more natural for SGD algorithms. This chapter uses the same Gramian definition as in Eq. (25) to make all results consistent.

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Acknowledgements

I would like to thank Nicolas Mayoraz and Li Zhang for helpful comments and suggestions.

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Authors and Affiliations

  1. Google Research, Mountain View, CA, USA

    Steffen Rendle

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  1. Steffen Rendle
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Correspondence to Steffen Rendle .

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Editors and Affiliations

  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Francesco Ricci

  2. Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Lior Rokach

  3. Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

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Rendle, S. (2022). Item Recommendation from Implicit Feedback. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-2197-4_4

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  • DOI: https://doi.org/10.1007/978-1-0716-2197-4_4

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