Aspect-driven User Preference and News Representation Learning for News Recommendation

TL;DR

This paper introduces ANRS, a news recommendation system that learns fine-grained aspect-level representations of user preferences and news content, leading to improved recommendation accuracy.

Contribution

It proposes a novel aspect-level encoding approach for both users and news, enhancing the detail and effectiveness of news recommendation models.

Findings

ANRS outperforms state-of-the-art methods on MIND dataset.

Aspect-level representations improve recommendation accuracy.

Extensive experiments validate the effectiveness of the proposed approach.

Abstract

News recommender systems are essential for helping users to efficiently and effectively find out those interesting news from a large amount of news. Most of existing news recommender systems usually learn topic-level representations of users and news for recommendation, and neglect to learn more informative aspect-level features of users and news for more accurate recommendation. As a result, they achieve limited recommendation performance. Aiming at addressing this deficiency, we propose a novel Aspect-driven News Recommender System (ANRS) built on aspect-level user preference and news representation learning. Here, news aspect is fine-grained semantic information expressed by a set of related words, which indicates specific aspects described by the news. In ANRS, news aspect-level encoder and user aspect-level encoder are devised to learn the fine-grained aspect-level representations of user's preferences and news characteristics respectively, which are fed into click predictor to judge the probability of the user clicking the candidate news. Extensive experiments are done on the commonly used real-world dataset MIND, which demonstrate the superiority of our method compared with representative and state-of-the-art methods.