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Novelty and diversity enhancement and evaluation in recommender systems and information retrieval

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SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

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https://doi.org/10.1145/2600428.2610382

Published: 03 July 2014 Publication History

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Abstract

The development and evaluation of Information Retrieval and Recommender Systems has traditionally focused on the relevance and accuracy of retrieved documents and recommendations, respectively. However, there is an increasing realization that accuracy alone might be a sub-optimal strategy for a successful user experience. Properties such as novelty and diversity have been explored in both fields for assessing and enhancing the usefulness of search results and recommendations. In this doctoral research we study the assessment and enhancement of both properties in the confluence of Information Retrieval and Recommender Systems.

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Cited By

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Yılmazer HÖzel S(2024)Diverse but Relevant Recommendations with Continuous Ant Colony OptimizationMathematics10.3390/math1216249712:16(2497)Online publication date: 13-Aug-2024
https://doi.org/10.3390/math12162497
Malitesta DCornacchia GPomo CMerra FDi Noia TDi Sciascio E(2024)Formalizing Multimedia Recommendation through Multimodal Deep LearningACM Transactions on Recommender Systems10.1145/3662738Online publication date: 29-Apr-2024
https://doi.org/10.1145/3662738
Shah CBender E(2024)Envisioning Information Access Systems: What Makes for Good Tools and a Healthy Web?ACM Transactions on the Web10.1145/364946818:3(1-24)Online publication date: 26-Feb-2024
https://dl.acm.org/doi/10.1145/3649468
Show More Cited By

Index Terms

Novelty and diversity enhancement and evaluation in recommender systems and information retrieval
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Information & Contributors

Information

Published In

SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval

July 2014

1330 pages

ISBN:9781450322577

DOI:10.1145/2600428

General Chairs:
Shlomo Geva
Queensland University of Technology
,
Andrew Trotman
University of Dunedin
,
Program Chairs:
Peter Bruza
Queensland University of Technology
,
Charles L.A. Clarke
University of Waterloo
,
Kal Järvelin
University of Tampere

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 July 2014

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SIGIR '14

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SIGIR

SIGIR '14: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 6 - 11, 2014

Queensland, Gold Coast, Australia

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SIGIR '14 Paper Acceptance Rate 82 of 387 submissions, 21%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

View all

Yılmazer HÖzel S(2024)Diverse but Relevant Recommendations with Continuous Ant Colony OptimizationMathematics10.3390/math1216249712:16(2497)Online publication date: 13-Aug-2024
https://doi.org/10.3390/math12162497
Malitesta DCornacchia GPomo CMerra FDi Noia TDi Sciascio E(2024)Formalizing Multimedia Recommendation through Multimodal Deep LearningACM Transactions on Recommender Systems10.1145/3662738Online publication date: 29-Apr-2024
https://doi.org/10.1145/3662738
Shah CBender E(2024)Envisioning Information Access Systems: What Makes for Good Tools and a Healthy Web?ACM Transactions on the Web10.1145/364946818:3(1-24)Online publication date: 26-Feb-2024
https://dl.acm.org/doi/10.1145/3649468
Al Jurdi WAbdo JDemerjian JMakhoul A(2024)Group Validation in Recommender Systems: Framework for Multi-layer Performance EvaluationACM Transactions on Recommender Systems10.1145/36408202:1(1-25)Online publication date: 19-Jan-2024
https://dl.acm.org/doi/10.1145/3640820
Zhao WLiu JRen RWen J(2023)Dense Text Retrieval Based on Pretrained Language Models: A SurveyACM Transactions on Information Systems10.1145/363787042:4(1-60)Online publication date: 18-Dec-2023
https://dl.acm.org/doi/10.1145/3637870
Li NLiu HMeng SLi Q(2023)FDRS: Federated Diversified Recommender System Based on Heterogeneous Graph Convolutional Network2023 IEEE International Conference on High Performance Computing & Communications, Data Science & Systems, Smart City & Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00043(257-264)Online publication date: 17-Dec-2023
https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00043
Lops PPolignano MMusto CSilletti ASemeraro G(2023) ClayRSInformation Systems10.1016/j.is.2023.102273119:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.is.2023.102273
Geng SHe XLiang GNiu BLiu SHe Y(2023)Accuracy-diversity optimization in personalized recommender system via trajectory reinforcement based bacterial colony optimizationInformation Processing and Management: an International Journal10.1016/j.ipm.2022.10320560:2Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.ipm.2022.103205
Anelli VDeldjoo YDi Noia TMalitesta DPaparella VPomo C(2023)Auditing Consumer- and Producer-Fairness in Graph Collaborative FilteringAdvances in Information Retrieval10.1007/978-3-031-28244-7_3(33-48)Online publication date: 17-Mar-2023
https://doi.org/10.1007/978-3-031-28244-7_3
Stitini OKaloun SBencharef O(2023)The Use of a Genetic Algorithm to Alleviate the Limited Content Issue in a Content-Based Recommendation SystemArtificial Intelligence and Smart Environment10.1007/978-3-031-26254-8_112(776-782)Online publication date: 8-Mar-2023
https://doi.org/10.1007/978-3-031-26254-8_112
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