Large-scale recommender system with compact latent factor model

This work devises a factorization model called compact latent factor model, in which we propose a compact representation to consider query, user and item in the model. The blend of information retrieval and collaborative filtering is a typical setting in many applications. The proposed model can incorporate various features into the model, and this work demonstrates that the proposed model can incorporate context-aware and content-based features to handle context-aware recommendation and cold-start problems, respectively. Besides recommendation accuracy, a critical problem concerning the computational cost emerges in practical situations. To tackle this problem, this work uses a buffer update scheme to allow the proposed model to process data incrementally, and provide a means to use historical data instances. Meanwhile, we use stochastic gradient descent algorithm along with sampling technique to optimize ranking loss, giving a competitive performance while considering scalability and deployment issues. The experimental results indicate that the proposed algorithm outperforms other alternatives on four datasets.