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Advanced Topics in Recommender Systems

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Abstract

Recommender systems are often used in a number of specialized settings that are not covered in previous chapters of this book. In many cases, the recommendations are performed in settings where there might be multiple users or multiple evaluation criteria. For example, consider a scenario where a group of tourists wish to take a vacation together. Therefore, they may want to obtain recommendations that match the overall interests of the various members in the group. In other scenarios, users may use multiple criteria to provide ratings to items. These variations in the problem formulation can sometimes make the prediction problem more challenging. In particular, we will study the following advanced variations of recommender systems in this chapter:

Keywords

  • Recommender System
  • Slot Machine
  • Recommendation Algorithm
  • Implicit Feedback
  • Bibliographic Note

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Figure 13.1:
Figure 13.2:
Figure 13.3:
Figure 13.4:
Figure 13.5:

Notes

  1. 1.

    If the payoffs lie in the range [0, Δ], then the deviation also needs to be scaled up by Δ.

  2. 2.

    In traditional recommender systems, items are more likely to have descriptive profiles than users.

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Aggarwal, C.C. (2016). Advanced Topics in Recommender Systems. In: Recommender Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-29659-3_13

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