poster

Adaptive bootstrapping of recommender systems using decision trees

Authors:

Nadav Golbandi,

Ronny LempelAuthors Info & Claims

WSDM '11: Proceedings of the fourth ACM international conference on Web search and data mining

Pages 595 - 604

https://doi.org/10.1145/1935826.1935910

Published: 09 February 2011 Publication History

Abstract

Recommender systems perform much better on users for which they have more information. This gives rise to a problem of satisfying users new to a system. The problem is even more acute considering that some of these hard to profile new users judge the unfamiliar system by its ability to immediately provide them with satisfying recommendations, and may quickly abandon the system when disappointed. Rapid profiling of new users by a recommender system is often achieved through a bootstrapping process - a kind of an initial interview - that elicits users to provide their opinions on certain carefully chosen items or categories. The elicitation process becomes particularly effective when adapted to users' responses, making best use of users' time by dynamically modifying the questions to improve the evolving profile. In particular, we advocate a specialized version of decision trees as the most appropriate tool for this task. We detail an efficient tree learning algorithm, specifically tailored to the unique properties of the problem. Several extensions to the tree construction are also introduced, which enhance the efficiency and utility of the method. We implemented our methods within a movie recommendation service. The experimental study delivered encouraging results, with the tree-based bootstrapping process significantly outperforming previous approaches.

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Cited By

Rajapakse DLeith D(2024)User Cold-Start Learning in Recommender Systems using Monte Carlo Tree SearchACM Transactions on Recommender Systems10.1145/36180023:1(1-23)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3618002
AbbasiRad EKeyvanpour MTohidi N(2024)Co-clustering method for cold start issue in collaborative filtering movie recommender systemMultimedia Tools and Applications10.1007/s11042-024-20103-3Online publication date: 13-Sep-2024
https://doi.org/10.1007/s11042-024-20103-3
Jin JChen XYe FYang MFeng YZhang WYu YWang JOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Lending interaction wings to recommender systems with conversational agentsProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667335(27951-27979)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3667335
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Index Terms

Adaptive bootstrapping of recommender systems using decision trees
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

WSDM '11: Proceedings of the fourth ACM international conference on Web search and data mining

February 2011

870 pages

ISBN:9781450304931

DOI:10.1145/1935826

General Chair:
Irwin King
CUHK, Hong Kong
,
Program Chairs:
Wolfgang Nejdl
L3S and University of Hannover, Germany
,
Hang Li
Microsoft Research Asia, China

Copyright © 2011 ACM.

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Published: 09 February 2011

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WSDM'11

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WSDM'11: Fourth ACM International Conference on Web Search and Data Mining

February 9 - 12, 2011

Hong Kong, China

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WSDM '11 Paper Acceptance Rate 83 of 372 submissions, 22%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Rajapakse DLeith D(2024)User Cold-Start Learning in Recommender Systems using Monte Carlo Tree SearchACM Transactions on Recommender Systems10.1145/36180023:1(1-23)Online publication date: 2-Aug-2024
https://dl.acm.org/doi/10.1145/3618002
AbbasiRad EKeyvanpour MTohidi N(2024)Co-clustering method for cold start issue in collaborative filtering movie recommender systemMultimedia Tools and Applications10.1007/s11042-024-20103-3Online publication date: 13-Sep-2024
https://doi.org/10.1007/s11042-024-20103-3
Jin JChen XYe FYang MFeng YZhang WYu YWang JOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Lending interaction wings to recommender systems with conversational agentsProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667335(27951-27979)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3667335
Qin YChang WZhang SYan Y(2023)Research on the Solutions to Cold-Start ProblemsProceedings of the 2023 2nd International Conference on Social Sciences and Humanities and Arts (SSHA 2023)10.2991/978-2-38476-062-6_3(12-18)Online publication date: 30-Jun-2023
https://doi.org/10.2991/978-2-38476-062-6_3
Alhosaini HAlharbi SWang XXu G(2023)API Recommendation For Mashup Creation: A Comprehensive SurveyThe Computer Journal10.1093/comjnl/bxad11267:5(1920-1940)Online publication date: 30-Nov-2023
https://doi.org/10.1093/comjnl/bxad112
He HZhang RZhang YRen J(2023)USBE: User-similarity based estimator for multimedia cold-start recommendationMultimedia Tools and Applications10.1007/s11042-023-15493-983:1(1127-1142)Online publication date: 2-Jun-2023
https://doi.org/10.1007/s11042-023-15493-9
Li YFurukawa T(2023)Information gain based dynamic support set construction for cold-start recommendationJournal of Intelligent Information Systems10.1007/s10844-023-00795-z61:3(717-737)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s10844-023-00795-z
Fuchs TBach JIser M(2023)Active Learning for SAT Solver BenchmarkingTools and Algorithms for the Construction and Analysis of Systems10.1007/978-3-031-30823-9_21(407-425)Online publication date: 22-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-30823-9_21
Rajapakse DLeith D(2022)Fast and Accurate User Cold-Start Learning Using Monte Carlo Tree SearchProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3546786(350-359)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3546786
Vie JRigaux TKashima H(2022)Variational Factorization Machines for Preference Elicitation in Large-Scale Recommender Systems2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020448(5607-5614)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020448
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