Recommending Personalized News in Short User Sessions


News organizations employ personalized recommenders to target news articles to specific readers and thus foster engagement. Existing approaches rely on extensive user profiles. However frequently possible, readers rarely authenticate themselves on news publishers' websites. This paper proposes an approach for such cases. It provides a basic degree of personalization while complying with the key characteristics of news recommendation including news popularity, recency, and the dynamics of reading behavior. We extend existing research on the dynamics of news reading behavior by focusing both on the progress of reading interests over time and their relations. Reading interests are considered in three levels: short-, medium-, and long-term. Combinations of these are evaluated in terms of added value to the recommendation's performance and ensured news variety. Experiments with 17-month worth of logs from a German news publisher show that most frequent relations between news reading interests are constant in time but their probabilities change. Recommendations based on combined short-term and long-term interests result in increased accuracy while recommendations based on combined short-term and medium-term interests yield higher news variety.

 author = {Epure, Elena Viorica and Kille, Benjamin and Ingvaldsen, Jon Espen and Deneckere, Rebecca and Salinesi, Camille and Albayrak, Sahin},
 title = {Recommending Personalized News in Short User Sessions},
 booktitle = {Proceedings of the Eleventh ACM Conference on Recommender Systems},
 series = {RecSys '17},
 year = {2017},
 isbn = {978-1-4503-4652-8},
 location = {Como, Italy},
 pages = {121--129},
 numpages = {9},
 url = {},
 doi = {10.1145/3109859.3109894},
 acmid = {3109894},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {markov processes, news reading behavior, news reading interests, personalization, recommender system, stationarity analysis},
Elena Viorica Epure, Benjamin Kille, Jon Espen Ingvaldsen, Rebecca Deneckere, Camille Salinesi, Sahin Albayrak
Proceedings of the Eleventh ACM Conference on Recommender Systems