Besides the success on object recognition, machine translation and system control in games, (deep) neural networks have achieved state-of-the-art results in collaborative filtering (CF) recently. Previous neural approaches for CF are either user-based or item-based, which cannot leverage all relevant information explicitly. We propose CF-UIcA, a neural co-autoregressive model for CF tasks, which exploit the structural autoregressiveness in the domains of both users and items. Furthermore, we separate the inherent dependence in this structure under a natural assumption and develop an efficient stochastic learning algorithm to handle large scale datasets. We evaluate CF-UIcA on two popular benchmarks: MovieLens 1M and Netflix, and achieve state-of-the-art predictive performance, which demonstrates the effectiveness of CF-UIcA.
之前的Neural CF方法(user-based or item-based)不能明显的平衡相关信息。这里提出了一种CF-UIc方法。
CF中两大关联：User-User Correlations (UUCs) and Item-Item Correlations (IICs)
隐藏变量模型：Matrix Factorization(MF) and neural network based models
MF methods take both UUCs and IICs in count implicitly as a prediction is the inner product of the latent vectors of the corresponding user and item.
(a)Predicting with a single User-User Correlation.
(b) Predicting with a single Item-Item Correlation.
(c) Predicting with multiple User-User Correlations.
(d) Predicting with multiple Item-Item Correlations.
(user, item, rating)三元组记为(i,j,Rij),评分范围1-K