本文主要用到了attention model and memory augmented netowrk，核心思想是利用整合local和global信息：前者用到了近邻用户；后者用到了全局的隐向量。
有意思的是：本文利用一个共同访问过的item，构建出两个用户之间关系 Eq 1，再利用这个关系（attentions）和全局的user representations，组合出（加权求和）新的user representations。这个新的表达，和原来旧的表达就可以共同用到对用户评分的预测。
所谓的multip Hops，指的是在上述新的user representations的基础上，多在memory network迭代几次，得到一个多次迭代后的user representation.
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Collaborative Memory Network for Recommendation Systems | |||||||||||
| 文献作者 | Travis Ebesu； Bin Shen； Yi Fang | ||||||||||
| 文献发表年限 | 2018 | ||||||||||
| 文献关键字 | |||||||||||
| Augmented; Memory Network; attention model | |||||||||||
| 摘要描述 | |||||||||||
| Recommendation systems play a vital role to keep users engaged with personalized content in modern online platforms. Deep learn- ing has revolutionized many research fields and there is a recent surge of interest in applying it to collaborative filtering (CF). However, existing methods compose deep learning architectures with the latent factor model ignoring a major class of CF models, neighborhood or memory-based approaches. We propose Collaborative Memory Networks (CMN), a deep architecture to unify the two classes of CF models capitalizing on the strengths of the global structure of latent factor model and local neighborhood-based structure in a nonlinear fashion. Motivated by the success of Memory Networks, we fuse a memory component and neural attention mechanism as the neighborhood component. The associative addressing scheme with the user and item memories in the memory module encodes complex user-item relations coupled with the neural attention mechanism to learn a user-item specific neighborhood. Finally, the output module jointly exploits the neighborhood with the user and item memories to produce the ranking score. Stacking multiple memory modules together yield deeper architectures capturing increasingly complex user-item relations. Furthermore, we show strong connections between CMN components, memory networks and the three classes of CF models. Comprehensive experimental results demonstrate the effectiveness of CMN on three public datasets outperforming competitive baselines. Qualitative visualization of the attention weights provide insight into the model’s recommendation process and suggest the presence of higher order interactions. | |||||||||||
