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Getting recommender systems to think outside the box

Published: 23 October 2009 Publication History

Abstract

We examine the case of over-specialization in recommender systems, which results from returning items that are too similar to those previously rated by the user. We propose Outside-The-Box (otb) recommendation, which takes some risk to help users make fresh discoveries, while maintaining high relevance. The proposed formalization relies on item regions and attempts to identify regions that are under-exposed to the user. We develop a recommendation algorithm which achieves a compromise between relevance and risk to find otb items. We evaluate this approach on the MovieLens data set and compare our otb recommendations against conventional recommendation strategies.

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Cited By

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  • (2024)Multidimensional Insights into Recommender Systems: A Systematic Review of Evaluation Metrics and Thematic ApplicationsSoftware Engineering Methods Design and Application10.1007/978-3-031-70285-3_29(382-403)Online publication date: 23-Oct-2024
  • (2022)Collaborative Rank Aggregation in Recommendation SystemsProcedia Computer Science10.1016/j.procs.2022.09.281207:C(2213-2222)Online publication date: 1-Jan-2022
  • (2022)Evolution of recommender paradigm optimization over timeJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2019.06.00834:4(1047-1059)Online publication date: Apr-2022
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    cover image ACM Conferences
    RecSys '09: Proceedings of the third ACM conference on Recommender systems
    October 2009
    442 pages
    ISBN:9781605584355
    DOI:10.1145/1639714
    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 2009

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    Author Tags

    1. OTB
    2. diversity
    3. outside the box
    4. recommendation
    5. serendipity

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    RecSys '09
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    RecSys '09: Third ACM Conference on Recommender Systems
    October 23 - 25, 2009
    New York, New York, USA

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

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    Cited By

    View all
    • (2024)Multidimensional Insights into Recommender Systems: A Systematic Review of Evaluation Metrics and Thematic ApplicationsSoftware Engineering Methods Design and Application10.1007/978-3-031-70285-3_29(382-403)Online publication date: 23-Oct-2024
    • (2022)Collaborative Rank Aggregation in Recommendation SystemsProcedia Computer Science10.1016/j.procs.2022.09.281207:C(2213-2222)Online publication date: 1-Jan-2022
    • (2022)Evolution of recommender paradigm optimization over timeJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2019.06.00834:4(1047-1059)Online publication date: Apr-2022
    • (2021)An Algorithm for Recommending Groceries Based on an Item Ranking Method2021 International Conference on Intelligent Technologies (CONIT)10.1109/CONIT51480.2021.9498399(1-7)Online publication date: 25-Jun-2021
    • (2021)A fairness-aware multi-stakeholder recommender systemWorld Wide Web10.1007/s11280-021-00946-824:6(1995-2018)Online publication date: 22-Sep-2021
    • (2021)Explanation-Based Serendipitous Recommender System (EBSRS)International Conference on Innovative Computing and Communications10.1007/978-981-16-3071-2_1(1-18)Online publication date: 29-Aug-2021
    • (2020)Designing for serendipity in a university course recommendation systemProceedings of the Tenth International Conference on Learning Analytics & Knowledge10.1145/3375462.3375524(350-359)Online publication date: 23-Mar-2020
    • (2020)CHESTNUT: Improve Serendipity in Movie Recommendation by an Information Theory-Based Collaborative Filtering ApproachHuman Interface and the Management of Information. Interacting with Information10.1007/978-3-030-50017-7_6(78-95)Online publication date: 10-Jul-2020
    • (2020)Early Findings from a Large-Scale User Study of CHESTNUT: Validations and ImplicationsHuman Interface and the Management of Information. Interacting with Information10.1007/978-3-030-50017-7_5(65-77)Online publication date: 10-Jul-2020
    • (2019)Online Platforms and Cultural Diversity in the Audiovisual SectorsAudiovisual Industries and Diversity10.4324/9780429427534-6(100-118)Online publication date: 20-Mar-2019
    • Show More Cited By

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