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Content-Based Recommender Systems

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Recommender Systems

Abstract

The collaborative systems discussed in the previous chapters use the correlations in the ratings patterns across users to make recommendations. On the other hand, these methods do not use item attributes for computing predictions. This would seem rather wasteful; after all, if John likes the futuristic science fiction movie Terminator, then there is a very good chance that he might like a movie from a similar genre, such as Aliens. In such cases, the ratings of other users may not be required to make meaningful recommendations.

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Notes

  1. 1.

    The exact recommendation method used by IMDb is proprietary and not known to the author. The description here is intended only for illustrative purposes.

  2. 2.

    For structured data, the centroid of the group may be used.

  3. 3.

    A different approach in collaborative filtering is to leverage user-user rules. For user-user rules, the antecedents and consequents may both contain the ratings of specific users. Refer to section 3.3 of Chapter 3.

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  1. IBM T.J. Watson Research Center, Yorktown Heights, NY, USA

    Charu C. Aggarwal

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Aggarwal, C.C. (2016). Content-Based Recommender Systems. In: Recommender Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-29659-3_4

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  • DOI: https://doi.org/10.1007/978-3-319-29659-3_4

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