Factored MDPs for detecting topics of user sessions

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Authors

Recommender systems aim to capture interests of users to provide tailored recommendations. User interests are however often unique and depend on many unobservable factors including a user's mood and the local weather. We take a contextual session-based approach and propose a sequential framework using factored Markov decision processes (fMDPs) to detect the user's goal (the topic) of a session. We show that an independence assumption on the attributes of items leads to a set of independent models that can be optimised efficiently. Our approach results in interpretable topics that can be effectively turned into recommendations. Empirical results on a real world click log from a large e-commerce company exhibit highly accurate topic prediction rates of about 90%. Translating our approach into a topic-driven recommender system outperforms several baseline competitors.
OriginalspracheEnglisch
TitelRecSys 2014 - Proceedings of the 8th ACM Conference on Recommender Systems
Anzahl der Seiten8
VerlagAssociation for Computing Machinery, Inc
Erscheinungsdatum06.10.2014
Seiten33-40
ISBN (Print)978-1-4503-2668-1
DOIs
PublikationsstatusErschienen - 06.10.2014
Extern publiziertJa
Veranstaltung8th ACM Conference on Recommender Systems - RecSys2014 - Crowne Plaza hotel in Foster City, Foster City, USA / Vereinigte Staaten
Dauer: 06.10.201410.10.2014
Konferenznummer: 8
https://recsys.acm.org/recsys14/

DOI

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