.png){kind=icon}
返回
Dual-Embedding based Deep Latent Factor Models for Recommendation
.png){kind=icon}
PRE
AI
DOI：10.1145/3447395.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 the 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 article proposes a dual-embedding based deep latent factor method for recommendation with implicit feedback. In addition to learning a primitive 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) and propose attentive neural methods to discriminate the importance of interacted users/items for dual-embedding learning. We design two dual-embedding based deep latent factor models, DELF and DESEQ, for pure collaborative filtering and temporal collaborative filtering (i.e., sequential recommendation), respectively. The novel attempt of the proposed models is to capture each user-item interaction with four deep representations that are subtly fused for preference prediction. We conducted extensive experiments on four real-world datasets. The results verify the effectiveness of user/item dual embeddings and the superior performance of our methods on item recommendation.
Keyword:
Latent factor models
sequential recommendation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.8
论文数:
1.3K
被引数:
4.4K
机构
引用论文
Seismic anisotropy indicates organized melt beneath the Mid-Atlantic Ridge aids seafloor spreading.png)
Geology
OAAI
GeologyIF0
OAAI分享收藏
查看更多内容