Abstract: SetRank is a novel Setwise Bayesian collaborative ranking model that can model implicit feedback data more closely to real-world scenarios. However, SetRank only considers collaborative information and lacks effective use of content information. In order to solve the above problems, a factorization machine model based on Setwise ranking, named SRFMs, is proposed. Drawing on the Setwise ranking proposed in SetRank to solve the problem of missing negative samples in implicit feedback, a factorization machine is chosen as a predictor to model content information, and model user preferences from the perspective of optimal item ranking. Furthermore, to improve the fixed feature representation and the lack of higher-order feature interactions in standard FM, inspired by IFM, SR-DIFM model is constructed to incorporate the SRFMs by combining input-aware networks with neural networks. Experimental results on two real-world datasets demonstrate that the proposed  model outperforms the state-of-art model in terms of evaluation metrics including HR, NDCG and mAP, and can improve the accuracy of recommendations which make better use of user and item content information while solving the recommendation problem under implicit feedback. 

Key words: implicit feedback, content information, learning to rank, factorization machine, neural network