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User-centered evaluation of strategies for recommending sequences of points of interest to groups

Published: 10 September 2019 Publication History

Abstract

Most recommender systems (RSs) predict the preferences of individual users; however, in certain scenarios, recommendations need to be made for a group of users. Tourism is a popular domain for group recommendations because people often travel in groups and look for point of interest (POI) sequences for their visits during a trip. In this study, we present different strategies that can be used to recommend POI sequences for groups. In addition, we introduce novel approaches, including a strategy called Split Group, which allows groups to split into smaller groups during a trip. We compared all strategies in a user study with 40 real groups. Our results proved that there was a significant difference in the quality of recommendations generated by using the different strategies. Most groups were willing to split temporarily during a trip, even when they were traveling with persons close to them. In this case, Split Group generated the best recommendations for different evaluation criteria. We use these findings to propose improvements for group recommendation strategies in the tourism domain.

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  • (2024)Accommodation Recommendation on Shared Platforms Considering Bidirectional Selection and Review MechanismsInternational Journal on Artificial Intelligence Tools10.1142/S021821302350051333:01Online publication date: 23-Feb-2024
  • (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
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    cover image ACM Other conferences
    RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems
    September 2019
    635 pages
    ISBN:9781450362436
    DOI:10.1145/3298689
    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: 10 September 2019

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    Author Tags

    1. group recommendation
    2. preference aggregation
    3. recommender system
    4. sequence
    5. social choice strategy
    6. user study

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    RecSys '19
    RecSys '19: Thirteenth ACM Conference on Recommender Systems
    September 16 - 20, 2019
    Copenhagen, Denmark

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    RecSys '19 Paper Acceptance Rate 36 of 189 submissions, 19%;
    Overall Acceptance Rate 254 of 1,295 submissions, 20%

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    Cited By

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    • (2024)Accommodation Recommendation on Shared Platforms Considering Bidirectional Selection and Review MechanismsInternational Journal on Artificial Intelligence Tools10.1142/S021821302350051333:01Online publication date: 23-Feb-2024
    • (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
    • (2023)Evaluating explainable social choice-based aggregation strategies for group recommendationUser Modeling and User-Adapted Interaction10.1007/s11257-023-09363-034:1(1-58)Online publication date: 21-Jun-2023
    • (2023)Bias characterization, assessment, and mitigation in location-based recommender systemsData Mining and Knowledge Discovery10.1007/s10618-022-00913-537:5(1885-1929)Online publication date: 14-Feb-2023
    • (2022)Utilizing location-based social media for trip mining and recommendationProceedings of the 6th ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising10.1145/3557992.3567412(1-1)Online publication date: 1-Nov-2022
    • (2022)A Systematic Review of Interaction Design Strategies for Group Recommendation SystemsProceedings of the ACM on Human-Computer Interaction10.1145/35551616:CSCW2(1-51)Online publication date: 11-Nov-2022
    • (2022)Tutorial on Offline Evaluation for Group Recommender SystemsProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3547371(702-705)Online publication date: 12-Sep-2022
    • (2022)Group Decision-Making and Designing Group Recommender SystemsHandbook of e-Tourism10.1007/978-3-030-48652-5_57(941-963)Online publication date: 2-Sep-2022
    • (2022)Individual and Group Decision Making and Recommender SystemsRecommender Systems Handbook10.1007/978-1-0716-2197-4_21(789-832)Online publication date: 22-Apr-2022
    • (2022)Building effective recommender systems for touristsAI Magazine10.1002/aaai.1205743:2(209-224)Online publication date: 16-Jun-2022
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