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Recommending Personalized News in Short User Sessions
- Source :
- RecSys 2017, RecSys 2017, Aug 2017, Como, Italy. ⟨10.1145/nnnnnnn.nnnnnnn⟩, RecSys
- Publication Year :
- 2017
- Publisher :
- HAL CCSD, 2017.
-
Abstract
- International audience; News organizations employ personalized recommenders to target news articles to speciic readers and thus foster engagement. Existing approaches rely on extensive user prooles. However frequently possible, readers rarely authenticate themselves on news publishers' websites. is 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.
- Subjects :
- Computer science
media_common.quotation_subject
[INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS]
02 engineering and technology
Recommender system
News reading interests
Personalization
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
World Wide Web
News reading behavior
020204 information systems
Reading (process)
0202 electrical engineering, electronic engineering, information engineering
Added value
media_common
Markov processes
[INFO.INFO-WB]Computer Science [cs]/Web
Stationarity analysis
Popularity
Variety (cybernetics)
Dynamics (music)
[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]
Key (cryptography)
020201 artificial intelligence & image processing
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- RecSys 2017, RecSys 2017, Aug 2017, Como, Italy. ⟨10.1145/nnnnnnn.nnnnnnn⟩, RecSys
- Accession number :
- edsair.doi.dedup.....4cd7af35d9c833002b8d19143ee13d23