A Look Into News Avoidance Through AWRS: An Avoidance-Aware Recommender System

TL;DR

This paper introduces AWRS, an avoidance-aware news recommender system that considers user news avoidance behavior to improve recommendation relevance, validated across multiple language datasets.

Contribution

The paper presents a novel avoidance-aware recommender system that explicitly models news avoidance as a key factor in personalization.

Findings

AWRS outperforms existing recommendation methods.

Incorporating avoidance improves recommendation relevance.

Effective across multiple languages and datasets.

Abstract

In recent years, journalists have expressed concerns about the increasing trend of news article avoidance, especially within specific domains. This issue has been exacerbated by the rise of recommender systems. Our research indicates that recommender systems should consider avoidance as a fundamental factor. We argue that news articles can be characterized by three principal elements: exposure, relevance, and avoidance, all of which are closely interconnected. To address these challenges, we introduce AWRS, an Avoidance-Aware Recommender System. This framework incorporates avoidance awareness when recommending news, based on the premise that news article avoidance conveys significant information about user preferences. Evaluation results on three news datasets in different languages (English, Norwegian, and Japanese) demonstrate that our method outperforms existing approaches.