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Dynamically-optimized context in recommender systems

Published: 09 May 2005 Publication History

Abstract

Traditional approaches to recommender systems have not taken into account situational information when making recommendations, and this seriously limits the relevance of the results. This paper advocates context-awareness as a promising approach to enhance the performance of recommenders, and introduces a mechanism to realize this approach. We present a framework that separates the contextual concerns from the actual recommendation module, so that contexts can be readily shared across applications. More importantly, we devise a learning algorithm to dynamically identify the optimal set of contexts for a specific recommendation task and user. An extensive series of experiments has validated that our system is indeed able to learn both quickly and accurately.

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Cited By

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  • (2022)How to select and weight context dimensions conditions for context-aware recommendation?Expert Systems with Applications: An International Journal10.1016/j.eswa.2021.115176182:COnline publication date: 9-Apr-2022
  • (2018)Sparse Linear Method Based Top-N Course Recommendation System with Expert Knowledge and L 0 RegularizationHuman Centered Computing10.1007/978-3-319-74521-3_15(130-138)Online publication date: 23-Jan-2018
  • (2016)An Education Driven Model for Non-Communicable Diseases CareHandbook of Research on Advancing Health Education through Technology10.4018/978-1-4666-9494-1.ch017(391-418)Online publication date: 2016
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cover image ACM Conferences
MDM '05: Proceedings of the 6th international conference on Mobile data management
May 2005
329 pages
ISBN:1595930418
DOI:10.1145/1071246
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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New York, NY, United States

Publication History

Published: 09 May 2005

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Author Tags

  1. context weight
  2. machine learning
  3. recommender system
  4. user feedback

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Cited By

View all
  • (2022)How to select and weight context dimensions conditions for context-aware recommendation?Expert Systems with Applications: An International Journal10.1016/j.eswa.2021.115176182:COnline publication date: 9-Apr-2022
  • (2018)Sparse Linear Method Based Top-N Course Recommendation System with Expert Knowledge and L 0 RegularizationHuman Centered Computing10.1007/978-3-319-74521-3_15(130-138)Online publication date: 23-Jan-2018
  • (2016)An Education Driven Model for Non-Communicable Diseases CareHandbook of Research on Advancing Health Education through Technology10.4018/978-1-4666-9494-1.ch017(391-418)Online publication date: 2016
  • (2012)A proactive personalised mobile recommendation system using analytic hierarchy process and Bayesian networkJournal of Internet Services and Applications10.1007/s13174-012-0061-33:2(195-214)Online publication date: 20-Jul-2012
  • (2011)The social cameraProceedings of the 16th international conference on Intelligent user interfaces10.1145/1943403.1943408(13-22)Online publication date: 13-Feb-2011
  • (2010)Research on Time Synchronization in Cluster Robots Based on Wireless NetworkProceedings of the 2010 International Conference on Electrical and Control Engineering10.1109/iCECE.2010.444(1807-1810)Online publication date: 25-Jun-2010
  • (2010)Using context history to personalize a resource recommender via a genetic algorithm2010 10th International Conference on Intelligent Systems Design and Applications10.1109/ISDA.2010.5687064(965-970)Online publication date: Nov-2010
  • (2010)Mining context-related sequential patterns for recommendation systems2010 International Conference on Information Retrieval & Knowledge Management (CAMP)10.1109/INFRKM.2010.5466905(270-275)Online publication date: Mar-2010
  • (2010)A Proactive Personalized Mobile News Recommendation SystemProceedings of the 2010 Developments in E-systems Engineering10.1109/DeSE.2010.40(207-212)Online publication date: 6-Sep-2010
  • (2010)Context-Aware News Recommender in Mobile Hybrid P2P NetworkProceedings of the 2010 2nd International Conference on Computational Intelligence, Communication Systems and Networks10.1109/CICSyN.2010.48(54-59)Online publication date: 28-Jul-2010
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