Learning implicit user interest hierarchy for context in personalization
Proceedings of the 8th international conference on Intelligent user interfaces
The Journal of Machine Learning Research
Time weight collaborative filtering
Proceedings of the 14th ACM international conference on Information and knowledge management
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
A framework for projected clustering of high dimensional data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Personalized news recommendation based on click behavior
Proceedings of the 15th international conference on Intelligent user interfaces
Towards context-aware personalization and a broad perspective on the semantics of news articles
Proceedings of the fourth ACM conference on Recommender systems
SCENE: a scalable two-stage personalized news recommendation system
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
News recommendation via hypergraph learning: encapsulation of user behavior and news content
Proceedings of the sixth ACM international conference on Web search and data mining
Modeling and broadening temporal user interest in personalized news recommendation
Expert Systems with Applications: An International Journal
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In this paper, we initially provide an experimental study on the evolution of user interests in real-world news recommender systems, and then propose a novel recommendation approach, in which the long-term and short-term reading preferences of users are seamlessly integrated when recommending news items. Given a hierarchy of newly-published news articles, news groups that the user might prefer are differentiated using the long-term profile, and then in each selected news group, a list of news items are chosen based on the short-term user profile. Extensive empirical experiments on a collection of news articles obtained from various popular news websites demonstrate the efficacy of our method.