论文标题
使用用户的本地上下文支持本地新闻
Using user's local context to support local news
论文作者
论文摘要
由于在线新闻来源的扩散，美国本地报纸在过去15年内一直在过去15年中遭受大量读者保留和业务损失。本地媒体公司开始根据订阅来减轻此问题，从而从广告支持的业务模型转变为一种。借助此订阅模型，需要增加用户参与度和个性化，而推荐系统是这些新闻公司实现此目标的一种方法。但是，在这种情况下，使用专注于用户全局偏好的标准建模方法是不合适的，因为用户的本地偏好表现出了一些特定特征，这些特征不一定与新闻中的长期或全局偏好相匹配。我们的研究使用基于本地新闻文章和与不同本地新闻类别有关的本地新闻文章和文章的建议探索了一种基于本地化的建议方法。在本地报纸的新闻数据集上进行的实验表明，这些本地模型，尤其是某些类别的项目，确实确实为个性化提供了更准确性和有效性，这反过来又可能导致更多的用户参与本地新闻内容。
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.
