本文主要是content + CF推荐方法。
题目中所谓的explicit和implicit不是指用户行为，而是指user/item attribute之间的属性关系。
本文的主要强调的特点是：coupling；具体做法是：构建一个function计算user attributes和item attributes各个维度之间的关系，构建了一个coupling matrix
最后基于上述的matrix，进行CNN操作（有一个local的和一个global的：即一个有卷积操作，一个没有）。
借用NCF的思想，融合CF，将用户的点击行为表达成一个向量；
最后再用FC结合attribute coupling向量和NCF得到的向量。
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| CoupledCF: Learning Explicit and Implicit User-item Couplings in Recommendation for Deep Collaborative Filtering | |||||||||||
| 文献作者 | Quangui Zhang; Longbing Cao | ||||||||||
| 文献发表年限 | 2018 | ||||||||||
| 文献关键字 | |||||||||||
| non-IID; content based; CF based; CNN; NCF | |||||||||||
| 摘要描述 | |||||||||||
| Non-IID recommender system discloses the nature of recommendation and has shown its potential in improving recommendation quality and addressing issues such as sparsity and cold start. It leverages existing work that usually treats users/items as independent while ignoring the rich couplings within and between users and items, leading to limited performance improvement. In reality, users/items are related with various couplings existing within and between users and items, which may better explain how and why a user has personalized preference on an item. This work builds on non-IID learning to propose a neural user-item coupling learning for collaborative filtering, called CoupledCF. CoupledCF jointly learns explicit and implicit couplings within/between users and items w.r.t. user/item attributes and deep features for deep CF recommendation. Empirical results on two real-world large datasets show that CoupledCF significantly outperforms two latest neural recommenders: neural matrix factorization and Google’s Wide&Deep network. | |||||||||||
