  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.
