Using user's local context to support local news

TL;DR

This paper proposes a localized session-based recommendation system tailored for local news, improving personalization accuracy and user engagement by focusing on local preferences and news categories.

Contribution

It introduces a novel localized recommendation approach that considers local user preferences and news categories, enhancing personalization for local news platforms.

Findings

Local models improve recommendation accuracy.

Category-specific recommendations increase user engagement.

Localized approach outperforms global preference models.

Abstract

American local newspapers have been experiencing a large loss of reader retention and business within the past 15 years due to the proliferation of online news sources. Local media companies are starting to shift from an advertising-supported business model to one based on subscriptions to mitigate this problem. With this subscription model, there is a need to increase user engagement and personalization, and recommender systems are one way for these news companies to accomplish this goal. However, using standard modeling approaches that focus on users' global preferences is not appropriate in this context because the local preferences of users exhibit some specific characteristics which do not necessarily match their long-term or global preferences in the news. Our research explores a localized session-based recommendation approach, using recommendations based on local news articles and articles pertaining to the different local news categories. Experiments performed on a news dataset from a local newspaper show that these local models, particularly certain categories of items, do indeed provide more accuracy and effectiveness for personalization which, in turn, may lead to more user engagement with local news content.