文献类型：会议论文

英文题名：A Hybrid Neural Network Model with Non-linear Factorization Machines for Collaborative Recommendation

作者：Liu, Yu[1];Guo, Weibin[1];Zang, Dawei[1];Li, Zongyin[2]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Inst Sci & Technol Dev, Shanghai 200237, Peoples R China

会议论文集：24th China Conference on Information Retrieval (CCIR)

会议日期：SEP 27-29, 2018

会议地点：Guangxi Normal Univ, Guilin, PEOPLES R CHINA

主办单位：Guangxi Normal Univ

语种：英文

外文关键词：Collaborative filtering; Neural networks; Deep learning; Factorization machines; Representation learning

摘要：In recent years, deep learning models have proven able to learn effective representation in many applications. However, the exploration of deep learning on recommender systems are relatively little. Although some recent work has utilized deep learning models to make recommendation, they primarily employed it to learn abstract representation of auxiliary information and used matrix factorization to model the interactions between user and item features. Especially, the application of deep learning models to learn user-item interaction function is very new and there are few attempts to this direction. In this paper, we propose a novel model Non-Linear Factorization Machine (NLFM) for modelling user-item interaction function and a hybrid deep model named AE-NLFM for collaborative recommendation. NLFM leverages neural networks to learn non-linear feature interaction and is more expressive than FM [15]. Extensive experiments on three real-world datasets show that our proposed AE-NLFM significantly outperforms the state-of-the-art methods.