short-paper

Towards Results-level Proportionality for Multi-objective Recommender Systems

Authors:

Ladislav Peska,

Patrik DokoupilAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1963 - 1968

https://doi.org/10.1145/3477495.3531787

Published: 07 July 2022 Publication History

Abstract

The main focus of our work is the problem of multiple objectives optimization (MOO) while providing a final list of recommendations to the user. Currently, system designers can tune MOO by setting importance of individual objectives, usually in some kind of weighted average setting. However, this does not have to translate into the presence of such objectives in the final results. In contrast, in our work we would like to allow system designers or end-users to directly quantify the required relative ratios of individual objectives in the resulting recommendations, e.g., the final results should have 60% relevance, 30% diversity and 10% novelty. If individual objectives are transformed to represent quality on the same scale, these result conditioning expressions may greatly contribute towards recommendations tuneability and explainability as well as user's control over recommendations.

To achieve this task, we propose an iterative algorithm inspired by the mandates allocation problem in public elections. The algorithm is applicable as long as per-item marginal gains of individual objectives can be calculated. Effectiveness of the algorithm is evaluated on several settings of relevance-novelty-diversity optimization problem. Furthermore, we also outline several options to scale individual objectives to represent similar value for the user.

Supplementary Material

MP4 File (SIGIR2022_sp1355.mp4)

Short presentation of paper: Towards Results-level Proportionality for Multi-objective Recommender Systems

Download
11.93 MB

References

[1]

Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher, and Edward Malthouse. 2021. User-Centered Evaluation of Popularity Bias in Recommender Systems. Association for Computing Machinery, New York, NY, USA, 119--129. https://doi.org/10.1145/3450613.3456821

Digital Library

[2]

Charu C. Aggarwal. 2016. Neighborhood-Based Collaborative Filtering. Springer International Publishing, Cham, 29--70. https://doi.org/10.1007/978--3--319--29659--3_2

[3]

Keith Bradley and Barry Smyth. 2001. Improving recommendation diversity. In Proceedings of the twelfth Irish conference on artificial intelligence and cognitive science, Maynooth, Ireland, Vol. 85. Citeseer, 141--152.

[4]

Minmin Chen. 2021. Exploration in Recommender Systems. Association for Computing Machinery, New York, NY, USA, 551--553. https://doi.org/10.1145/3460231.3474601

Digital Library

[5]

Tiago Cunha, Ioannis Partalas, and Phong Nguyen. 2021. Juggler: Multi-Stakeholder Ranking with Meta-Learning. In Workshop of Multi-Objective Recommender Systems (MORS'21), in conjunction with the 15th ACM Conference on Recommender Systems, RecSys, Vol. 21. 2021.

[6]

Van Dang and W. Bruce Croft. 2012. Diversity by Proportionality: An Election- Based Approach to Search Result Diversification. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval (Portland, Oregon, USA) (SIGIR '12). Association for Computing Machinery, New York, NY, USA, 65--74. https://doi.org/10.1145/2348283.2348296

Digital Library

[7]

Mouzhi Ge, Carla Delgado-Battenfeld, and Dietmar Jannach. 2010. Beyond Accuracy: Evaluating Recommender Systems by Coverage and Serendipity. In Proceedings of the Fourth ACM Conference on Recommender Systems (Barcelona, Spain) (RecSys '10). Association for Computing Machinery, New York, NY, USA, 257--260. https://doi.org/10.1145/1864708.1864761

Digital Library

[8]

F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Trans. Interact. Intell. Syst. 5, 4, Article 19 (dec 2015), 19 pages. https://doi.org/10.1145/2827872

Digital Library

[9]

Kalervo Järvelin and Jaana Kekäläinen. 2002. Cumulated Gain-Based Evaluation of IR Techniques. ACM Trans. Inf. Syst. 20, 4 (oct 2002), 422--446. https://doi.org/10.1145/582415.582418

Digital Library

[10]

Naime Ranjbar Kermany, Weiliang Zhao, Jian Yang, Jia Wu, and Luiz Pizzato. 2020. An Ethical Multi-Stakeholder Recommender System Based on Evolutionary Multi-Objective Optimization. In 2020 IEEE International Conference on Services Computing (SCC). 478--480. https://doi.org/10.1109/SCC49832.2020.00074

[11]

Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. Computer 42, 8 (aug 2009), 30--37. https://doi.org/10.1109/MC.2009.263

Digital Library

[12]

Ladislav Malecek and Ladislav Peska. 2021. Fairness-Preserving Group Recommendations With User Weighting. Association for Computing Machinery, New York, NY, USA, 4--9. https://doi.org/10.1145/3450614.3461679

Digital Library

[13]

Judith Masthoff. 2011. Group Recommender Systems: Combining Individual Models. Springer US, Boston, MA, 677--702. https://doi.org/10.1007/978-0--387--85820--3_21

[14]

Jiaona Pang, Jun Guo, andWei Zhang. 2019. Using Multi-objective Optimization to Solve the Long Tail Problem in Recommender System. In Advances in Knowledge Discovery and Data Mining, Qiang Yang, Zhi-Hua Zhou, Zhiguo Gong, Min-Ling Zhang, and Sheng-Jun Huang (Eds.). Springer International Publishing, Cham, 302--313.

[15]

Marco Tulio Ribeiro, Anisio Lacerda, Adriano Veloso, and Nivio Ziviani. 2012. Pareto-Efficient Hybridization for Multi-Objective Recommender Systems. In Proceedings of the Sixth ACMConference on Recommender Systems (Dublin, Ireland) (RecSys '12). Association for Computing Machinery, New York, NY, USA, 19--26. https://doi.org/10.1145/2365952.2365962

Digital Library

[16]

Sinan Seymen, Himan Abdollahpouri, and Edward C Malthouse. 2021. A unified optimization toolbox for solving popularity bias, fairness, and diversity in recommender systems. In Workshop of Multi-Objective Recommender Systems (MORS'21), in conjunction with the 15th ACM Conference on Recommender Systems, RecSys, Vol. 21. 2021.

[17]

Tomá Skopal. 2020. On Visualizations in the Role of Universal Data Representation. Association for Computing Machinery, New York, NY, USA, 362--367. https://doi.org/10.1145/3372278.3390743

Digital Library

[18]

Alain Starke, Christoph Trattner, Hedda Bakken, Martin Johannessen, and Vegard Solberg. 2021. The cholesterol factor: Balancing accuracy and health in recipe recommendation through a nutrient-specific metric. In CEUR Workshop Proceedings, Vol. 2959.

[19]

Súul Vargas. 2011. New approaches to diversity and novelty in recommender systems. In Fourth BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2011) 4. 8--13.

[20]

Saúl Vargas, Linas Baltrunas, Alexandros Karatzoglou, and Pablo Castells. 2014. Coverage, Redundancy and Size-Awareness in Genre Diversity for Recommender Systems. In Proceedings of the 8th ACM Conference on Recommender Systems (Foster City, Silicon Valley, California, USA) (RecSys '14). Association for Computing Machinery, New York, NY, USA, 209--216. https://doi.org/10.1145/2645710.2645743

Digital Library

[21]

Saúl Vargas and Pablo Castells. 2011. Rank and Relevance in Novelty and Diversity Metrics for Recommender Systems. In Proceedings of the Fifth ACM Conference on Recommender Systems (Chicago, Illinois, USA) (RecSys '11). Association for Computing Machinery, New York, NY, USA, 109--116. https://doi.org/10.1145/2043932.2043955

Digital Library

[22]

Mu Zhu. 2004. Recall, precision and average precision. Department of Statistics and Actuarial Science, University of Waterloo, Waterloo 2, 30 (2004), 6.

Cited By

Dokoupil PBoratto LPeska L(2024)User Perceptions of Diversity in Recommender SystemsProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659555(212-222)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3627043.3659555
Dokoupil PPeska LBoratto LHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)SM-RS: Single- and Multi-Objective Recommendations with Contextual Impressions and Beyond-Accuracy Propensity ScoresProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657863(988-995)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657863
Wang ZShao YHe JLiu J(2024)Distribution-Aware Diversification for Personalized Re-ranking in RecommendationWeb and Big Data10.1007/978-981-97-7235-3_5(65-81)Online publication date: 28-Aug-2024
https://doi.org/10.1007/978-981-97-7235-3_5
Show More Cited By

Index Terms

Towards Results-level Proportionality for Multi-objective Recommender Systems
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Information retrieval diversity
    2. Retrieval tasks and goals
      1. Recommender systems

Recommendations

A survey of recommender systems with multi-objective optimization
Abstract
Recommender systems have been widely applied to several domains and applications to assist decision making by recommending items tailored to user preferences. One of the popular recommendation algorithms is the model-based approach ...
Multi-objective optimization with recommender systems: A systematic review
Abstract
Recommender systems have become essential in modern information systems and Internet applications by delivering personalized and pertinent content to users. While conventional recommendation algorithms usually prioritize optimizing a ...
Highlights
- Explores MORS’s importance in delivering personalized and diverse recommendations.
Inverted PBI in MOEA/D and its impact on the search performance on multi and many-objective optimization
GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

MOEA/D decomposes a multi-objective optimization problem into a number of single objective optimization problems. Each single objective optimization problem is defined by a scalarizing function using a weight vector. In MOEA/D, there are several ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
UNED
,
Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
Ben Carterette
Spotify
,
J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

Copyright © 2022 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 the author(s) 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].

Sponsors

SIGIR: ACM Special Interest Group on Information Retrieval

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

Charles University Grant Agency
Czech Science Foundation
Charles University

Conference

SIGIR '22

Sponsor:

SIGIR

SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
285
Total Downloads

Downloads (Last 12 months)36
Downloads (Last 6 weeks)9

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Dokoupil PBoratto LPeska L(2024)User Perceptions of Diversity in Recommender SystemsProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659555(212-222)Online publication date: 22-Jun-2024
https://dl.acm.org/doi/10.1145/3627043.3659555
Dokoupil PPeska LBoratto LHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)SM-RS: Single- and Multi-Objective Recommendations with Contextual Impressions and Beyond-Accuracy Propensity ScoresProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657863(988-995)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657863
Wang ZShao YHe JLiu J(2024)Distribution-Aware Diversification for Personalized Re-ranking in RecommendationWeb and Big Data10.1007/978-981-97-7235-3_5(65-81)Online publication date: 28-Aug-2024
https://doi.org/10.1007/978-981-97-7235-3_5
Dokoupil PPeska LBoratto L(2023)Looks Can Be Deceiving: Linking User-Item Interactions and User’s Propensity Towards Multi-Objective RecommendationsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608848(912-918)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608848
Dokoupil PPeska LBoratto LChen HDuh WHuang HKato MMothe JPoblete B(2023)Rows or Columns? Minimizing Presentation Bias When Comparing Multiple Recommender SystemsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592056(2354-2358)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3592056

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten