# Session-based Social Recommendation via Dynamic Graph Attention Networks

## TL;DR

This paper introduces a dynamic graph attention neural network for session-based social recommendation, effectively modeling users' evolving interests and context-dependent social influences to improve recommendation accuracy.

## Contribution

It presents a novel neural network model combining recurrent and graph attention mechanisms to capture dynamic user interests and social influences in online communities.

## Key findings

- Outperforms several baseline models on real-world datasets.
- Effectively models dynamic interests and social influence.
- Scalable to large-scale data.

## Abstract

Online communities such as Facebook and Twitter are enormously popular and have become an essential part of the daily life of many of their users. Through these platforms, users can discover and create information that others will then consume. In that context, recommending relevant information to users becomes critical for viability. However, recommendation in online communities is a challenging problem: 1) users' interests are dynamic, and 2) users are influenced by their friends. Moreover, the influencers may be context-dependent. That is, different friends may be relied upon for different topics. Modeling both signals is therefore essential for recommendations. We propose a recommender system for online communities based on a dynamic-graph-attention neural network. We model dynamic user behaviors with a recurrent neural network, and context-dependent social influence with a graph-attention neural network, which dynamically infers the influencers based on users' current interests. The whole model can be efficiently fit on large-scale data. Experimental results on several real-world data sets demonstrate the effectiveness of our proposed approach over several competitive baselines including state-of-the-art models.