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Recommending content using side information

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Abstract

Collaborative Filtering methods predict user interests and make recommendations just by using the rating matrix. However, in practice there is extensive side information about users and items, such as the age of the user, the actors in a movie, or the abstract of a journal article. In this paper, a novel model called Collaborative Poisson Factorization with Side-information (CPFS) is proposed which extends CTPF by incorporating richer kinds of side information conditionally as a prior to the model. CPFS is a monolithic hybridization model that combines features from different data sources into a single recommendation algorithm. We develop a Gibbs sampler and also a Variational method with closed-form updates for the inference of CPFS and demonstrate its applicability on a range of datasets including movies, books, academic papers, and travel. The extension improves prediction quality, especially in the cold start scenario. The connections between side information and topics are also intuitive.

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Notes

  1. CTM models topic correlation by using logistic normal distribution instead of Dirichlet distribution.

  2. Both CTPF and CPFS refer to this variable as 𝜖.

  3. https://grouplens.org/datasets/movielens/

  4. This method can be replaced with any available word embedding methods.

  5. The values of X, i and j are related. For example, if X = 𝜃 then i = d (I = D) and j = l (J = L).

  6. For example, \( \alpha ^{\theta }_{d,k} = {\prod }_{l=1}^{L}(\lambda ^{\theta }_{k,l})^{f^{\theta }_{d,l}}\).

  7. https://grouplens.org/datasets/movielens/1m/

  8. http://www.omdbapi.com/

  9. http://mallet.cs.umass.edu/topics.php

  10. http://www.citeulike.org/

  11. http://www.tripadvisor.com/

  12. http://www.cs.cmu.edu/~dbamman/booksummaries.html

  13. http://www.bookcrossing.com/

  14. https://doi.org/10.7910/DVN/HFM52Uhttps://doi.org/10.7910/DVN/Y9DYL6https://doi.org/10.7910/DVN/0BEMJLhttps://doi.org/10.7910/DVN/H8AR2H

  15. The code can be found at https://github.com/premgopalan/collabtm

  16. The code can be found at https://www.cs.toronto.edu/~rsalakhu/BPMF.html

  17. The code can be found at https://github.com/eelxpeng/CollaborativeVAEhttps://github.com/eelxpeng/CollaborativeVAE

  18. The code can be found at https://github.com/hexiangnan/neural_collaborative_filtering

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran

    Rabeh Ravanifard & Abdolreza Mirzaei

  2. Department of Information Technology, Monash University, Melbourne, Australia

    Wray Buntine

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  1. Rabeh Ravanifard
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  2. Wray Buntine
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  3. Abdolreza Mirzaei
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Correspondence to Abdolreza Mirzaei.

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Ravanifard, R., Buntine, W. & Mirzaei, A. Recommending content using side information. Appl Intell 51, 3353–3374 (2021). https://doi.org/10.1007/s10489-020-01945-4

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