核心思想：
基于NCF的框架，将传统的user/item embedding结合利用（用用户访问过的item embedding来表达的user embedding;或者用访问过该物品的用户的user embedding表达的item embedding）。这样一个有两个user embedding以及两个item embedding。然后相互组合，确定最后的predication。
其中，用用户访问过的item embedding来表达的user embedding的过程中，用到了attention model。
A novel attempt of DELF is that we employ dual embeddings to learn four kinds of deep interactions for each user-item pair, which enables DELF to generalize two principled CF methods, i.e., NCF and NSVD. To the best of our knowledge, this work is the first neural approach that leverages dual user and item embeddings for recommendation with implicit feedback.
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| DELF: A Dual-Embedding based Deep Latent Factor Model for Recommendation | |||||||||||
| 文献作者 | Weiyu Cheng | ||||||||||
| 文献发表年限 | 2018 | ||||||||||
| 文献关键字 | |||||||||||
| NCF; NSVD; 用item embedding表达user embedding, 反之亦可; 传统的user embedding + 用item embedding表达的user embedding; | |||||||||||
| 摘要描述 | |||||||||||
| Among various recommendation methods, latent factor models are usually considered to be state-of-the-art techniques, which aim to learn user and item embeddings for predicting user-item preferences. When applying latent factor models to recommendation with implicit feedback, the quality of embeddings always suffers from inadequate positive feedback and noisy negative feedback. Inspired by the idea of NSVD that represents users based on their interacted items, this paper proposes a dual- embedding based deep latent factor model named DELF for recommendation with implicit feedback. In addition to learning a single embedding for a user (resp. item), we represent each user (resp. item) with an additional embedding from the perspective of the interacted items (resp. users). We employ an attentive neural method to discriminate the importance of interacted users/items for dual- embedding learning. We further introduce a neural network architecture to incorporate dual embeddings for recommendation. A novel attempt of DELF is to model each user-item interaction with four deep representations that are subtly fused for preference prediction. We conducted extensive experiments on real-world datasets. The results verify the effectiveness of user/item dual embeddings and the superior performance of DELF on item recommendation. | |||||||||||
