Personalized Transaction Kernels for Recommendation Using MCTS

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review


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 languageEnglish
Title of host publicationKI 2019 : Advances in Artificial Intelligence - 42nd German Conference on AI, Proceedings
EditorsChristoph Benzmüller, Heiner Stuckenschmidt
Number of pages15
Place of PublicationWiesbaden
Publication date01.09.2019
ISBN (Print)978-3-030-30178-1
ISBN (Electronic)978-3-030-30179-8
Publication statusPublished - 01.09.2019
EventGerman Conference on Artificial Intelligence, KI 2019 - Kassel, Germany
Duration: 23.09.201926.09.2019
Conference number: 42