Pairwise Interactive Graph Attention Network for Context-Aware Recommendation

TL;DR

This paper introduces PIGAT, a graph neural network model that captures dynamic user interests and item attractions by modeling user-item interactions as a graph, improving context-aware recommendations especially for long-tail items.

Contribution

The paper proposes PIGAT, a novel GNN-based model that incorporates attention and confidence embeddings to enhance feature representation in context-aware recommendation systems.

Findings

PIGAT outperforms existing models on three real-world datasets.

Attention mechanisms effectively capture user interests and item attractions.

Confidence embeddings improve handling of interactions over time.

Abstract

Context-aware recommender systems (CARS), which consider rich side information to improve recommendation performance, have caught more and more attention in both academia and industry. How to predict user preferences from diverse contextual features is the core of CARS. Several recent models pay attention to user behaviors and use specifically designed structures to extract adaptive user interests from history behaviors. However, few works take item history interactions into consideration, which leads to the insufficiency of item feature representation and item attraction extraction. From these observations, we model the user-item interaction as a dynamic interaction graph (DIG) and proposed a GNN-based model called Pairwise Interactive Graph Attention Network (PIGAT) to capture dynamic user interests and item attractions simultaneously. PIGAT introduces the attention mechanism to consider the importance of each interacted user/item to both the user and the item, which captures user interests, item attractions and their influence on the recommendation context. Moreover, confidence embeddings are applied to interactions to distinguish the confidence of interactions occurring at different times. Then more expressive user/item representations and adaptive interaction features are generated, which benefits the recommendation performance especially when involving long-tail items. We conduct experiments on three real-world datasets to demonstrate the effectiveness of PIGAT.