Paper accepted at European Conference on Machine Learning (ECML-PKDD) 2017

We have a paper accepted at ECML-PKDD 2017: “Content-Based Social Recommendation with Poisson Matrix Factorization” (Eliezer de Souza da Silva, Helge Langseth and Heri Ramampiaro). This is our first full paper resulting from our research on Poisson factorization and integration of multiple sources of information in a single recommendation model. If you have interest on the paper please email me and I will be happy to discuss.

Also, I am uploading the supplement of the paper here (you can find it also on my publications page)

Supplementary material for: “Content-Based Social
Recommendation with Poisson Matrix Factorization”

Abstract:

We introduce Poisson Matrix Factorization with Content and Social trust information (PoissonMF-CS), a latent variable probabilistic model for recommender systems with the objective of jointly modeling social trust, item content and user’s preference using Poisson matrix factorization framework. This probabilistic model is equivalent to collectively factorizing a non-negative user–item interaction matrix and a non-negative item–content matrix. The user–item matrix consists of sparse implicit (or explicit) interactions counts between user and item, and the item–content matrix consists of words or tags counts per item. The model imposes additional constraints given by the social ties between users, and the homophily effect on social networks — the tendency of people with similar preferences to be socially connected. Using this model we can account for and fine-tune the weight of content-based and social-based factors in the user preference. We develop approximate variational inference algorithm and perform experiments comparing PoissonMF-CS with competing models. The experimental evaluation indicates that PoissonMF-CS achieves superior predictive performance on held-out data for the top-$M$ recommendations task. Also, we observe that PoissonMF-CS generates compact latent representations when compared with alternative models while maintaining superior predictive performance.

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