Personalized Transaction Kernels for Recommendation Using MCTS

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Standard

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. Hrsg. / Christoph Benzmüller; Heiner Stuckenschmidt. Wiesbaden: Springer, 2019. S. 338-352 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 11793 LNAI).

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Harvard

Tavakol, M, Joppen, T, Brefeld, U & Fürnkranz, J 2019, Personalized Transaction Kernels for Recommendation Using MCTS. in C Benzmüller & H Stuckenschmidt (Hrsg.), KI 2019: Advances in Artificial Intelligence - 42nd German Conference on AI, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 11793 LNAI, Springer, Wiesbaden, S. 338-352, German Conference on Artificial Intelligence, KI 2019, Kassel, Deutschland, 23.09.19. https://doi.org/10.1007/978-3-030-30179-8_31

APA

Tavakol, M., Joppen, T., Brefeld, U., & Fürnkranz, J. (2019). Personalized Transaction Kernels for Recommendation Using MCTS. In C. Benzmüller, & H. Stuckenschmidt (Hrsg.), KI 2019: Advances in Artificial Intelligence - 42nd German Conference on AI, Proceedings (S. 338-352). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 11793 LNAI). Springer. https://doi.org/10.1007/978-3-030-30179-8_31

Vancouver

Tavakol M, Joppen T, Brefeld U, Fürnkranz J. Personalized Transaction Kernels for Recommendation Using MCTS. in Benzmüller C, Stuckenschmidt H, Hrsg., KI 2019: Advances in Artificial Intelligence - 42nd German Conference on AI, Proceedings. Wiesbaden: Springer. 2019. S. 338-352. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-30179-8_31

Bibtex

@inbook{55552a254a764340833cde48964c51c8,
title = "Personalized Transaction Kernels for Recommendation Using MCTS",
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.",
keywords = "MCTS, Personalization, Preference learning, Tensor kernel, Business informatics",
author = "Maryam Tavakol and Tobias Joppen and Ulf Brefeld and Johannes F{\"u}rnkranz",
year = "2019",
month = sep,
day = "1",
doi = "10.1007/978-3-030-30179-8_31",
language = "English",
isbn = "978-3-030-30178-1",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "338--352",
editor = "Christoph Benzm{\"u}ller and Heiner Stuckenschmidt",
booktitle = "KI 2019",
address = "Germany",
note = "German Conference on Artificial Intelligence, KI 2019, KI ; Conference date: 23-09-2019 Through 26-09-2019",
url = "https://www.ki2019.de/",

}

RIS

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 -

DOI