GLoCIM: Global-view Long Chain Interest Modeling for news recommendation

TL;DR

GLoCIM introduces a global-view long chain interest modeling approach that leverages a global click graph and collaborative filtering to enhance news recommendation accuracy.

Contribution

It proposes a novel method combining long chain interest extraction with neighbor interest integration using a gated network for improved news recommendation.

Findings

Significant performance improvement on real-world datasets.

Effective long chain interest modeling enhances user preference capture.

Collaborative interest integration boosts recommendation relevance.

Abstract

Accurately recommending candidate news articles to users has always been the core challenge of news recommendation system. News recommendations often require modeling of user interest to match candidate news. Recent efforts have primarily focused on extracting local subgraph information in a global click graph constructed by the clicked news sequence of all users. Howerer, the computational complexity of extracting global click graph information has hindered the ability to utilize far-reaching linkage which is hidden between two distant nodes in global click graph collaboratively among similar users. To overcome the problem above, we propose a Global-view Long Chain Interests Modeling for news recommendation (GLoCIM), which combines neighbor interest with long chain interest distilled from a global click graph, leveraging the collaboration among similar users to enhance news recommendation. We therefore design a long chain selection algorithm and long chain interest encoder to obtain global-view long chain interest from the global click graph. We design a gated network to integrate long chain interest with neighbor interest to achieve the collaborative interest among similar users. Subsequently we aggregate it with local news category-enhanced representation to generate final user representation. Then candidate news representation can be formed to match user representation to achieve news recommendation. Experimental results on real-world datasets validate the effectiveness of our method to improve the performance of news recommendation.