IACN: Influence-aware and Attention-based Co-evolutionary Network for Recommendation

TL;DR

This paper introduces IACN, a novel recommendation model that combines interaction and influence modeling to better capture evolving user interests in online communities, leading to improved recommendation accuracy.

Contribution

The paper presents a new influence-aware and attention-based co-evolutionary network that integrates interaction and influence signals for dynamic user embedding updates.

Findings

Outperforms state-of-the-art models in recommendation tasks.

Effectively captures user interest evolution from interactions and social influence.

Demonstrates significant improvements across multiple domains.

Abstract

Recommending relevant items to users is a crucial task on online communities such as Reddit and Twitter. For recommendation system, representation learning presents a powerful technique that learns embeddings to represent user behaviors and capture item properties. However, learning embeddings on online communities is a challenging task because the user interest keep evolving. This evolution can be captured from 1) interaction between user and item, 2) influence from other users in the community. The existing dynamic embedding models only consider either of the factors to update user embeddings. However, at a given time, user interest evolves due to a combination of the two factors. To this end, we propose Influence-aware and Attention-based Co-evolutionary Network (IACN). Essentially, IACN consists of two key components: interaction modeling and influence modeling layer. The interaction modeling layer is responsible for updating the embedding of a user and an item when the user interacts with the item. The influence modeling layer captures the temporal excitation caused by interactions of other users. To integrate the signals obtained from the two layers, we design a novel fusion layer that effectively combines interaction-based and influence-based embeddings to predict final user embedding. Our model outperforms the existing state-of-the-art models from various domains.