Article

Influence-based collaborative active learning

Authors:

Neil Rubens, Masashi SugiyamaAuthors Info & Claims

RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems

Pages 145 - 148

https://doi.org/10.1145/1297231.1297257

Published: 19 October 2007 Publication History

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Abstract

In order to learn a user's preferences in collaborative recommender systems it is crucial to select the most informative items for a user to rate. For example, rating a popular item will provide little discriminative information about user's preferences since most users like popular items. Existing approaches select the most informative items based primarily on items' uncertainty, but tend to ignore an important metric of coverage - the number of items for which we are able to accurately estimate preferences. Selecting an item based only on uncertainty will reduce the uncertainty of the selected item, but will not necessarily reduce the uncertainty of other items - which is the ultimate goal. Therefore, in order to reduce the uncertainty over all items, we propose to select items that are not only uncertain but are also influential. Experimental results demonstrate the advantages of the proposed approach.

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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
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Influence-based collaborative active learning

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Information & Contributors

Information

Published In

RecSys '07: Proceedings of the 2007 ACM conference on Recommender systems

October 2007

222 pages

ISBN:9781595937308

DOI:10.1145/1297231

General Chair:
Joseph A. Konstan
University of Minnesota, USA
,
Program Chairs:
John Riedl
University of Minnesota, USA
,
Barry Smyth
University College, Dublin, Ireland

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 19 October 2007

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RecSys07

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RecSys07: ACM Conference on Recommender Systems

October 19 - 20, 2007

MN, Minneapolis, USA

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

View all

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
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
Iovine ALops PNarducci Fde Gemmis MSemeraro GHung CHong JBechini ASong E(2021)Improving preference elicitation in a conversational recommender system with active learning strategiesProceedings of the 36th Annual ACM Symposium on Applied Computing10.1145/3412841.3442013(1375-1382)Online publication date: 22-Mar-2021
https://dl.acm.org/doi/10.1145/3412841.3442013
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Du ZTang JDing Y(2021)POLAR++: Active One-Shot Personalized Article RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.295372133:6(2709-2722)Online publication date: 1-Jun-2021
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Zhang CTaylor SCobb CSekhon J(2020)Active matrix factorization for surveysThe Annals of Applied Statistics10.1214/20-AOAS132214:3Online publication date: 1-Sep-2020
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