Personalized Transaction Kernels for Recommendation Using MCTS
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Personalized Transaction Kernels for Recommendation Using MCTS. / Tavakol, Maryam; Joppen, Tobias; Brefeld, Ulf et al.
KI 2019: Advances in Artificial Intelligence - 42nd German Conference on AI, Proceedings. ed. / Christoph Benzmüller; Heiner Stuckenschmidt. Wiesbaden : Springer, 2019. p. 338-352 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11793 LNAI).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Personalized Transaction Kernels for Recommendation Using MCTS
AU - Tavakol, Maryam
AU - Joppen, Tobias
AU - Brefeld, Ulf
AU - Fürnkranz, Johannes
N1 - Conference code: 42
PY - 2019/9/1
Y1 - 2019/9/1
N2 - We study pairwise preference data to model the behavior of users in online recommendation problems. We first propose a tensor kernel to model contextual transactions of a user in a joint feature space. The representation is extended to all users via hash functions that allow to effectively store and retrieve personalized slices of data and context. In order to quickly focus on the relevant properties of the next item to display, we propose the use of Monte-Carlo tree search on the learned preference values. Empirically, on real-world transaction data, both the preference models as well as the search tree exhibit excellent performance over baseline approaches.
AB - We study pairwise preference data to model the behavior of users in online recommendation problems. We first propose a tensor kernel to model contextual transactions of a user in a joint feature space. The representation is extended to all users via hash functions that allow to effectively store and retrieve personalized slices of data and context. In order to quickly focus on the relevant properties of the next item to display, we propose the use of Monte-Carlo tree search on the learned preference values. Empirically, on real-world transaction data, both the preference models as well as the search tree exhibit excellent performance over baseline approaches.
KW - MCTS
KW - Personalization
KW - Preference learning
KW - Tensor kernel
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=85072855644&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-30179-8_31
DO - 10.1007/978-3-030-30179-8_31
M3 - Article in conference proceedings
AN - SCOPUS:85072855644
SN - 978-3-030-30178-1
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 338
EP - 352
BT - KI 2019
A2 - Benzmüller, Christoph
A2 - Stuckenschmidt, Heiner
PB - Springer
CY - Wiesbaden
T2 - German Conference on Artificial Intelligence, KI 2019
Y2 - 23 September 2019 through 26 September 2019
ER -