Personalized Transaction Kernels for Recommendation Using MCTS.

Maryam Tavakol; Tobias Joppen; Ulf Brefeld; Johannes Fürnkranz

doi:10.1007/978-3-030-30179-8_31

Personalized Transaction Kernels for Recommendation Using MCTS.

Maryam Tavakol, Tobias Joppen, Ulf Brefeld, Johannes Fürnkranz

Uncertainty in Artificial Intelligence

Research output: Contribution to conference › Paper › Academic

Abstract

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.

Original language	English
Pages	338-352
Number of pages	15
DOIs	https://doi.org/10.1007/978-3-030-30179-8_31
Publication status	Published - 2019

Bibliographical note

DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

Keywords

MCTS
Personalization
Preference learning
Tensor kernel

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10.1007/978-3-030-30179-8_31

https://dblp.org/rec/conf/ki/TavakolJBF19

Cite this

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AU - Joppen, Tobias

AU - Brefeld, Ulf

AU - Fürnkranz, Johannes

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ER -

Personalized Transaction Kernels for Recommendation Using MCTS.

Abstract

Bibliographical note

Keywords

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