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Autor:
Tavakol, Maryam; Brefeld, Ulf:

Titel:
Factored MDPs for detecting the topic of user sessions

Quelle:
In: Association for Computing Machinery (Hrsg.): RecSys' 14 Foster City, CA : ACM (2014) , 33-40

URL des Volltextes:
https://www.kma.informatik.tu-darmstadt.de/fileadmin/user_upload/Group_KMA/kma_publications/recsy160-tavakolATS.pdf

Sprache:
Englisch

Dokumenttyp:
4. Beiträge in Sammelwerken; Tagungsband/Konferenzbeitrag/Proceedings

Schlagwörter:
Empfehlungssystem, Information Retrieval, Interesse, Nutzerverhalten, Prognose, Thema


Abstract(original):
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. (DIPF/Orig.)


DIPF-Abteilung:
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zuletzt verändert: 11.11.2016