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Exploiting contextual information in recommender systems

Published: 23 October 2008 Publication History

Abstract

Recommender Systems help an on-line user to tame information overload and are being used now in complex domains where it could be beneficial to exploit context-awareness, e.g., in travel recommendation. Technically, in Recommender Systems we can interpret context as a set of constraints or preferences over the usage of items determined by the contextual conditions (e.g., today it is raining or the user is in a particular location). In fact, there is a lack of approaches to deal effectively with contextual data. This thesis investigates some approaches to exploit context in Recommender Systems. It provides a general architecture of context-aware Recommender Systems and analyzes separate components of this model. The main focus is to investigate new approaches that can bring a real added value to users. In this paper I also describe my initial results on item selection and item weighting for context-dependent Collaborative Filtering (CF). Moreover, I shall present my ongoing research on CF hybridization using context.

References

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Cited By

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  • (2024)An Approach for Multi-Context-Aware Multi-Criteria Recommender Systems Based on Deep LearningIEEE Access10.1109/ACCESS.2024.342863012(99936-99948)Online publication date: 2024
  • (2023)Design and Implementation of Proactive Multi-Type Context-Aware Recommender System for Patients Suffering Diabetes2023 International Conference on Smart Applications, Communications and Networking (SmartNets)10.1109/SmartNets58706.2023.10216111(1-7)Online publication date: 25-Jul-2023
  • (2022)A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest RecommendationACM Transactions on Information Systems10.1145/350847840:4(1-35)Online publication date: 9-Mar-2022
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cover image ACM Conferences
RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems
October 2008
348 pages
ISBN:9781605580937
DOI:10.1145/1454008
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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Publication History

Published: 23 October 2008

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Author Tags

  1. context
  2. recommender systems

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RecSys08: ACM Conference on Recommender Systems
October 23 - 25, 2008
Lausanne, Switzerland

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

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Cited By

View all
  • (2024)An Approach for Multi-Context-Aware Multi-Criteria Recommender Systems Based on Deep LearningIEEE Access10.1109/ACCESS.2024.342863012(99936-99948)Online publication date: 2024
  • (2023)Design and Implementation of Proactive Multi-Type Context-Aware Recommender System for Patients Suffering Diabetes2023 International Conference on Smart Applications, Communications and Networking (SmartNets)10.1109/SmartNets58706.2023.10216111(1-7)Online publication date: 25-Jul-2023
  • (2022)A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest RecommendationACM Transactions on Information Systems10.1145/350847840:4(1-35)Online publication date: 9-Mar-2022
  • (2022)Simulating and Modeling the Risk of Conversational SearchACM Transactions on Information Systems10.1145/350735740:4(1-33)Online publication date: 24-Mar-2022
  • (2021)Multi-channel Tactile Feedback Based on User Finger SpeedProceedings of the ACM on Human-Computer Interaction10.1145/34885495:ISS(1-17)Online publication date: 5-Nov-2021
  • (2021)Ultrasound-driven Curveball in Table TennisProceedings of the ACM on Human-Computer Interaction10.1145/34885485:ISS(1-20)Online publication date: 5-Nov-2021
  • (2021)Personalised Services in Social SituationsProceedings of the ACM on Human-Computer Interaction10.1145/34329184:CSCW3(1-21)Online publication date: 5-Jan-2021
  • (2021)Tool combination model based on task sequence using an optimized orientation genetic algorithmEvolutionary Intelligence10.1007/s12065-021-00571-415:3(1619-1635)Online publication date: 14-Feb-2021
  • (2021)A Contextual Bayesian User Experience Model for Scholarly Recommender SystemsArtificial Intelligence in HCI10.1007/978-3-030-77772-2_10(139-165)Online publication date: 3-Jul-2021
  • (2019)Kernel Context Recommender System (KCR): A Scalable Context-Aware Recommender System AlgorithmIEEE Access10.1109/ACCESS.2019.28970037(24719-24737)Online publication date: 2019
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