Using Recommender Systems to Help Revitalize Local News.

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 and social media. This has led to a disturbing trend where local journalism and local news outlets are being forced out of business often leaving whole communities without a key source of credible information. This trend has a potentially broad societal impact as these key anchors of local trust and democracy are slowly becoming extinct. Local media companies are starting to shift from an advertising-supported business model to one based on subscriptions to mitigate their financial crises. But with strong competition from a variety of online news sources, these companies need to increase user engagement by providing significant added value. Providing more personalized content in the local context may be one way that these companies can succeed in this effort. Recommender system technologies are the primary enabling mechanisms for delivering such personalized content. However, using standard machine learning models 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. The overall goal of this research is to develop predictive models that more effectively derive user engagement through automatic personalization. Effective recommender systems may be among the tools that can help reverse the current decline of interest in local news sources. Our research explores approaches to learning localized models from user interaction data with news articles, particularly in news categories where there is intense local interest and there is a significant difference between users’ global and local news preferences. Specifically, we propose using such localized models in a session-based recommender system where the system can switch between users’ global and local preference models automatically when warranted. We report experiments performed on a news dataset from a local newspaper show that these local models, particularly the Life-and-Culture news category, do indeed provide more accuracy and effectiveness for personalization which, in turn, may lead to more user engagement with local news content.