Co-Factorization Model for Collaborative Filtering with Session-based Data
This is an incremental improvement for recommender systems that use session-based data.
The paper tackles the problem of matrix factorization's inability to capture strong item-item associations in collaborative filtering by combining MF with item2vec to incorporate localized item relationships into latent representations, resulting in improved performance on several datasets.
Matrix factorization (MF) is a common method for collaborative filtering. MF represents user preferences and item attributes by latent factors. Despite that MF is a powerful method, it suffers from not be able to identifying strong associations of closely related items. In this work, we propose a method for matrix factorization that can reflect the localized relationships between strong related items into the latent representations of items. We do it by combine two worlds: MF for collaborative filtering and item2vec for item-embedding. The proposed method is able to exploit item-item relations. Our experiments on several datasets demonstrates a better performance with the previous work.