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Reducing the sparsity of contextual information for recommender systems

Published: 09 September 2012 Publication History

Abstract

Our work focuses on the improvement of the accuracy of context-aware recommender systems. Contextual information showed to be promising factor in recommender systems. However, pure context-based recommender systems can not outperform other approaches mainly due to high sparsity of contextual information. We propose an idea to improve accuracy of context based recommender systems by context inference. Context inference is based on effect discovered by analyses of the context as a factor influencing user needs. Analyses of the news readers reveals existence of behavioural correlation which is the main pillar of proposed context inference. Method for context inference is based on collaborative filtering and clustering of web usage (as a non-discretizing alternative to association rules mining).

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  • (2017)Context-Awareness in Location Based Services in the Big Data EraMobile Big Data10.1007/978-3-319-67925-9_5(85-127)Online publication date: 1-Nov-2017

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cover image ACM Conferences
RecSys '12: Proceedings of the sixth ACM conference on Recommender systems
September 2012
376 pages
ISBN:9781450312707
DOI:10.1145/2365952
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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Published: 09 September 2012

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Author Tags

  1. clustering
  2. context
  3. recommender system
  4. user behaviour

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RecSys '12
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RecSys '12: Sixth ACM Conference on Recommender Systems
September 9 - 13, 2012
Dublin, Ireland

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RecSys '12 Paper Acceptance Rate 24 of 119 submissions, 20%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%

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  • (2017)Context-Awareness in Location Based Services in the Big Data EraMobile Big Data10.1007/978-3-319-67925-9_5(85-127)Online publication date: 1-Nov-2017

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