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Time- and Location-Sensitive Recommender Systems

Abstract

In many real scenarios, the buying and rating behaviors of customers are associated with temporal information. For example, the ratings in the Netflix Prize data set are associated with a “GradeDate” variable, and it was eventually shown [310] how the temporal component could be used to improve the rating predictions.

Keywords

  • Markov Model
  • Recommender System
  • Temporal Context
  • Sequential Pattern Mining
  • Recommendation Process

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 9.1
Figure 9.2

Notes

  1. 1.

    The original work [186] does not use a modulus in the denominator. We have added it in Equation 9.3 because omitting it does not make much sense in the case of negative similarity. Nevertheless, negative similarities in the peer item-group are rare in practical settings because the peers are defined as the most similar items.

  2. 2.

    In the discussion of section 3.6.4.6, the bias variables are absorbed within the factor matrices U and V by increasing the number of columns in each of the two factor matrices from k to (k + 2). However, in this exposition, we do not absorb the bias variables in the columns of the factor matrices. This is because of the more complex and special way in which bias variables are treated in temporal models. For example, Equation 3.21 of Chapter 3 and Equation 9.6 are identical, but they use somewhat different notations. It is important to keep these notational distinctions in mind to avoid confusion.

  3. 3.

    The work in [293] uses time-varying item factors.

  4. 4.

    Refer to the bibliographic notes for background on Markov chains.

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Aggarwal, C.C. (2016). Time- and Location-Sensitive Recommender Systems. In: Recommender Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-29659-3_9

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