Related papers: Dual Side Deep Context-aware Modulation for Social Recommendation

Dual Side Deep Context-aware Modulation for Social Recommendation

URL: http://arxiv.org/abs/2103.08976v1
Date: Tue, 16 Mar 2021 11:08:30 GMT
Title: Dual Side Deep Context-aware Modulation for Social Recommendation
Authors: Bairan Fu and Wenming Zhang and Guangneng Hu and Xinyu Dai and Shujian Huang and Jiajun Chen
Abstract summary: We propose a novel graph neural network to model the social relation and collaborative relation. On top of high-order relations, a dual side deep context-aware modulation is introduced to capture the friends' information and item attraction.
Score: 50.59008227281762
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Social recommendation is effective in improving the recommendation performance by leveraging social relations from online social networking platforms. Social relations among users provide friends' information for modeling users' interest in candidate items and help items expose to potential consumers (i.e., item attraction). However, there are two issues haven't been well-studied: Firstly, for the user interests, existing methods typically aggregate friends' information contextualized on the candidate item only, and this shallow context-aware aggregation makes them suffer from the limited friends' information. Secondly, for the item attraction, if the item's past consumers are the friends of or have a similar consumption habit to the targeted user, the item may be more attractive to the targeted user, but most existing methods neglect the relation enhanced context-aware item attraction. To address the above issues, we proposed DICER (Dual Side Deep Context-aware Modulation for SocialRecommendation). Specifically, we first proposed a novel graph neural network to model the social relation and collaborative relation, and on top of high-order relations, a dual side deep context-aware modulation is introduced to capture the friends' information and item attraction. Empirical results on two real-world datasets show the effectiveness of the proposed model and further experiments are conducted to help understand how the dual context-aware modulation works.