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Explicit relevance models in intent-oriented information retrieval diversification

Authors:

Pablo Castells,

David ValletAuthors Info & Claims

SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval

Pages 75 - 84

https://doi.org/10.1145/2348283.2348297

Published: 12 August 2012 Publication History

Abstract

The intent-oriented search diversification methods developed in the field so far tend to build on generative views of the retrieval system to be diversified. Core algorithm components in particular redundancy assessment are expressed in terms of the probability to observe documents, rather than the probability that the documents be relevant. This has been sometimes described as a view considering the selection of a single document in the underlying task model. In this paper we propose an alternative formulation of aspect-based diversification algorithms which explicitly includes a formal relevance model. We develop means for the effective computation of the new formulation, and we test the resulting algorithm empirically. We report experiments on search and recommendation tasks showing competitive or better performance than the original diversification algorithms. The relevance-based formulation has further interesting properties, such as unifying two well-known state of the art algorithms into a single version. The relevance-based approach opens alternative possibilities for further formal connections and developments as natural extensions of the framework. We illustrate this by modeling tolerance to redundancy as an explicit configurable parameter, which can be set to better suit the characteristics of the IR task, or the evaluation metrics, as we illustrate empirically.

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Cited By

Rafique WHafid AQadir J(2023)Developing smart city services using intent‐aware recommendation systems: A surveyTransactions on Emerging Telecommunications Technologies10.1002/ett.472834:4Online publication date: 12-Jan-2023
https://doi.org/10.1002/ett.4728
Jesse MBauer CJannach D(2022)Intra-list similarity and human diversity perceptions of recommendations: the details matterUser Modeling and User-Adapted Interaction10.1007/s11257-022-09351-w33:4(769-802)Online publication date: 12-Dec-2022
https://doi.org/10.1007/s11257-022-09351-w
Figuerêdo JCalumby R(2022)Unsupervised query-adaptive implicit subtopic discovery for diverse image retrieval based on intrinsic cluster qualityMultimedia Tools and Applications10.1007/s11042-022-13050-481:30(42991-43011)Online publication date: 4-May-2022
https://doi.org/10.1007/s11042-022-13050-4
Show More Cited By

Index Terms

Explicit relevance models in intent-oriented information retrieval diversification
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Published In

cover image ACM Conferences

SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval

August 2012

1236 pages

ISBN:9781450314725

DOI:10.1145/2348283

General Chair:
William Hersh
Oregon Health & Science University, USA
,
Program Chairs:
Jamie Callan
Carnegie Mellon University, USA
,
Yoelle Maarek
Yahoo! Research, Israel
,
Mark Sanderson
Royal Melbourne Institute of Technology, Australia

Copyright © 2012 ACM.

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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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 August 2012

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SIGIR '12

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SIGIR

SIGIR '12: The 35th International ACM SIGIR conference on research and development in Information Retrieval

August 12 - 16, 2012

Oregon, Portland, USA

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

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Cited By

Rafique WHafid AQadir J(2023)Developing smart city services using intent‐aware recommendation systems: A surveyTransactions on Emerging Telecommunications Technologies10.1002/ett.472834:4Online publication date: 12-Jan-2023
https://doi.org/10.1002/ett.4728
Jesse MBauer CJannach D(2022)Intra-list similarity and human diversity perceptions of recommendations: the details matterUser Modeling and User-Adapted Interaction10.1007/s11257-022-09351-w33:4(769-802)Online publication date: 12-Dec-2022
https://doi.org/10.1007/s11257-022-09351-w
Figuerêdo JCalumby R(2022)Unsupervised query-adaptive implicit subtopic discovery for diverse image retrieval based on intrinsic cluster qualityMultimedia Tools and Applications10.1007/s11042-022-13050-481:30(42991-43011)Online publication date: 4-May-2022
https://doi.org/10.1007/s11042-022-13050-4
Castells PHurley NVargas S(2021)Novelty and Diversity in Recommender SystemsRecommender Systems Handbook10.1007/978-1-0716-2197-4_16(603-646)Online publication date: 22-Nov-2021
https://doi.org/10.1007/978-1-0716-2197-4_16
Kaya MBridge DTintarev N(2020)Ensuring Fairness in Group Recommendations by Rank-Sensitive Balancing of RelevanceProceedings of the 14th ACM Conference on Recommender Systems10.1145/3383313.3412232(101-110)Online publication date: 22-Sep-2020
https://dl.acm.org/doi/10.1145/3383313.3412232
Kaya MBridge DBogers TSaid ABrusilovsky PTikk D(2019)A comparison of calibrated and intent-aware recommendationsProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3347045(151-159)Online publication date: 10-Sep-2019
https://dl.acm.org/doi/10.1145/3298689.3347045
Kaya MBridge DHung CPapadopoulos G(2019)Community-aware diversification of recommendationsProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297439(1639-1646)Online publication date: 8-Apr-2019
https://dl.acm.org/doi/10.1145/3297280.3297439
Cai HZhang F(2019)BS-SC: An Unsupervised Approach for Detecting Shilling Profiles in Collaborative Recommender SystemsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.2946247(1-1)Online publication date: 2019
https://doi.org/10.1109/TKDE.2019.2946247
Figueira PBelém FAlmeida JGonçalves MDownie SZhu QHahn J(2019)Automatic generation of initial reading listsProceedings of the 18th Joint Conference on Digital Libraries10.1109/JCDL.2019.00011(1-10)Online publication date: 2-Jun-2019
https://dl.acm.org/doi/10.1109/JCDL.2019.00011
Garba AKhalid SKhusro SUllah I(2019)Search Results Diversification based on Subtopics Attention Network2019 2nd International Conference on Communication, Computing and Digital systems (C-CODE)10.1109/C-CODE.2019.8681009(126-131)Online publication date: Mar-2019
https://doi.org/10.1109/C-CODE.2019.8681009
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