Gradient boosting factorization machines

Recommendation techniques have been well developed in the past decades. Most of them build models only based on user item rating matrix. However, in real world, there is plenty of auxiliary information available in recommendation systems. We can utilize these information as additional features to improve recommendation performance. We refer to recom- mendation with auxiliary information as context-aware rec- ommendation. Context-aware Factorization Machines (FM) is one of the most successful context-aware recommendation models. FM models pairwise interactions between all fea- tures, in such way, a certain feature latent vector is shared to compute the factorized parameters it involved. In prac- tice, there are tens of context features and not all the pair- wise feature interactions are useful. Thus, one important challenge for context-aware recommendation is how to effec- tively select \good" interaction features. In this paper, we focus on solving this problem and propose a greedy interac- tion feature selection algorithm based on gradient boosting. Then we propose a novel Gradient Boosting Factorization Machine (GBFM) model to incorporate feature selection al- gorithm with Factorization Machines into a unified frame- work. The experimental results on both synthetic and real datasets demonstrate the efficiency and effectiveness of our algorithm compared to other state-of-the-art methods.