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Context-Sensitive Recommender Systems

Abstract

For me context is the key – from that comes the understanding

Keywords

  • Recommender System
  • Rating Matrix
  • Data Cube
  • Tensor Factorization
  • Implicit Feedback

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 8.1
Figure 8.2
Figure 8.3
Figure 8.4
Figure 8.5

Notes

  1. 1.

    In the traditional database context, the notions of dimension and attribute mean the same thing. In this case, however, they do not mean the same thing. A set of attributes is associated with a dimension.

  2. 2.

    This similarity might not be obvious at first because the two equations do not use the same notation. Each k-dimensional factor vector \(\overline{v_{i}}\) of the factorization machine is equivalent to one of the k-dimensional rows of either the user, item, or context factor matrix in Equation 8.6.

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Aggarwal, C.C. (2016). Context-Sensitive Recommender Systems. In: Recommender Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-29659-3_8

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  • DOI: https://doi.org/10.1007/978-3-319-29659-3_8

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