关键词: 推荐系统, 深度学习, 自动编码机, 排序学习, 局部结构

Abstract: Most previous collaborative filtering approaches employ the matrix factorization techniques to learn latent user feature profiles and item feature profiles. However, the matrix factorization based methods may not make full use of the rating information, leading to unsatisfying performance. In recent years, deep learning has been approved to be able to find good representations in natural language processing, speech recognition, image classification, and so on. Moreover, the rating matrix not only contains the rating information, but also the ranking information. Furthermore, the local structure of rating matrix is considered to learn latent users' and items' representations. To this end, this paper proposes an ensemble local representation learning based recommendation algorithm. In the proposed algorithm, the randomly selected anchor is used to obtain local matrix, and then AutoEncoder is applied to learn latent representations and obtain submodel. Finally, the ranking information is incorporated by defining (user, item) pairs. The experimental results on two data sets validate the effectiveness of the proposed algorithm, compared with matrix factorization algorithms. Also, the algorithm outperforms the state-of-the-art model LCR (local collaborative ranking), which indicates the superiority of applying deep learning techniques to recommendation.

Key words: recommender systems, deep learning, AutoEncoder, ranking learning, local structure