Incremental multi-dimensional recommender systems: co-factorization vs tensors

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.