Incremental Multi-Dimensional Recommender Systems: Co-Factorization vs Tensors


Miguel Sozinho Ramalho, João Vinagre, Alípio Mário Jorge, Rafaela Bastos ;
Proceedings of the 2nd Workshop on Online Recommder Systems and User Modeling, PMLR 109:21-35, 2019.


The present paper sets a milestone on incremental recommender systems approaches by comparing several state-of-the-art algorithms with two different mathematical foundations - matrix and tensor factorization. Traditional Pairwise Interaction Tensor Factorization is revisited and converted into a scalable and incremental option that yields the best predictive power. A novel tensor inspired approach is described. Finally, experiments compare contextless vs context-aware scenarios, the impact of noise on the algorithms, discrepancies between time complexity and execution times, and are run on five different datasets from three different recommendation areas - music, gross retail and garment. Relevant conclusions are drawn that aim to help choosing the most appropriate algorithm to use when faced with a novel recommender tasks.

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