skip to main content
10.1145/3298689.3346998acmotherconferencesArticle/Chapter ViewAbstractPublication PagesrecsysConference Proceedingsconference-collections
research-article

A pareto-efficient algorithm for multiple objective optimization in e-commerce recommendation

Published: 10 September 2019 Publication History

Abstract

Recommendation with multiple objectives is an important but difficult problem, where the coherent difficulty lies in the possible conflicts between objectives. In this case, multi-objective optimization is expected to be Pareto efficient, where no single objective can be further improved without hurting the others. However existing approaches to Pareto efficient multi-objective recommendation still lack good theoretical guarantees.
In this paper, we propose a general framework for generating Pareto efficient recommendations. Assuming that there are formal differentiable formulations for the objectives, we coordinate these objectives with a weighted aggregation. Then we propose a condition ensuring Pareto efficiency theoretically and a two-step Pareto efficient optimization algorithm. Meanwhile the algorithm can be easily adapted for Pareto Frontier generation and fair recommendation selection. We specifically apply the proposed framework on E-Commerce recommendation to optimize GMV and CTR simultaneously. Extensive online and offline experiments are conducted on the real-world E-Commerce recommender system and the results validate the Pareto efficiency of the framework.
To the best of our knowledge, this work is among the first to provide a Pareto efficient framework for multi-objective recommendation with theoretical guarantees. Moreover, the framework can be applied to any other objectives with differentiable formulations and any model with gradients, which shows its strong scalability.

References

[1]
Kamelia Aryafar, Devin Guillory, and Liangjie Hong. 2017. An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy. In Proceedings of the ADKDD'17 (ADKDD'17). ACM, New York, NY, USA, Article 10, 6 pages.
[2]
Stephen Boyd and Lieven Vandenberghe. 2004. Convex Optimization. Cambridge University Press, New York, NY, USA.
[3]
Rasmus Bro and Sijmen De Jong. 2015. A Fast Non-negativity-constrained Least Squares Algorithm. Journal of Chemometrics 11, 5 (2015), 393--401.
[4]
Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender. 2005. Learning to Rank Using Gradient Descent. In Proceedings of the 22Nd International Conference on Machine Learning (ICML '05). ACM, New York, NY, USA, 89--96.
[5]
Chris J.C. Burges. 2010. From RankNet to LambdaRank to LambdaMART: An Overview. Technical Report.
[6]
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. 2007. Learning to Rank: From Pairwise Approach to Listwise Approach. In Proceedings of the 24th International Conference on Machine Learning (ICML '07). ACM, New York, NY, USA, 129--136.
[7]
Abhijnan Chakraborty, Saptarshi Ghosh, Niloy Ganguly, and Krishna P. Gummadi. 2017. Optimizing the Recency-Relevancy Trade-off in Online News Recommendations. In Proceedings of the 26th International Conference on World Wide Web (WWW '17). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 837--846.
[8]
Xiang Chen, Bowei Chen, and Mohan Kankanhalli. 2017. Optimizing Trade-offs Among Stakeholders in Real-Time Bidding by Incorporating Multimedia Metrics. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '17). 205--214.
[9]
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah. 2016. Wide & Deep Learning for Recommender Systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems (DLRS 2016). 7--10.
[10]
Laizhong Cui, Peng Ou, Xianghua Fu, Zhenkun Wen, and Nan Lu. 2017. A Novel Multi-objective Evolutionary Algorithm for Recommendation Systems. J. Parallel Distrib. Comput. 103, C (May 2017), 53--63.
[11]
Jean-Antoine Désidèri. 2009. Multiple-Gradient Descent Algorithm (MGDA).
[12]
Huizhong Duan, ChengXiang Zhai, Jinxing Cheng, and Abhishek Gattani. 2013. Supporting Keyword Search in Product Database: A Probabilistic Approach. Proc. VLDB Endow. 6, 14 (Sept. 2013), 1786--1797.
[13]
Yoav Freund, Raj Iyer, Robert E. Schapire, and Yoram Singer. 2003. An Efficient Boosting Algorithm for Combining Preferences. J. Mach. Learn. Res. 4 (Dec. 2003), 933--969.
[14]
Neil J. Hurley. 2013. Personalised Ranking with Diversity. In Proceedings of the 7th ACM Conference on Recommender Systems (RecSys '13). 379--382.
[15]
Tamas Jambor and Jun Wang. 2010. Optimizing Multiple Objectives in Collaborative Filtering. In Proceedings of the Fourth ACM Conference on Recommender Systems (RecSys '10). ACM, New York, NY, USA, 55--62.
[16]
Dietmar Jannach, Zeynep Karakaya, and Fatih Gedikli. 2012. Accuracy Improvements for Multi-criteria Recommender Systems. In Proceedings of the 13th ACM Conference on Electronic Commerce (EC '12). ACM, New York, NY, USA, 674--689.
[17]
Marius Kaminskas and Derek Bridge. 2016. Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems. ACM Trans. Interact. Intell. Syst. 7, 1, Article 2 (Dec. 2016), 42 pages.
[18]
Shubhra Kanti Karmaker Santu, Parikshit Sondhi, and ChengXiang Zhai. 2017. On Application of Learning to Rank for E-Commerce Search. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '17). ACM, New York, NY, USA, 475--484.
[19]
Beibei Li, Anindya Ghose, and Panagiotis G. Ipeirotis. 2011. Towards a Theory Model for Product Search. In Proceedings of the 20th International Conference on World Wide Web (WWW '11). ACM, New York, NY, USA, 327--336.
[20]
Ping Li, Chris J.C. Burges, and Qiang Wu. 2008. Learning to Rank Using Classification and Gradient Boosting (advances in neural information processing systems 20 ed.). Technical Report.
[21]
Xiao Ma, Liqin Zhao, Guan Huang, Zhi Wang, Zelin Hu, Xiaoqiang Zhu, and Kun Gai. 2018. Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR '18). ACM, New York, NY, USA, 1137--1140.
[22]
Sean M. McNee, John Riedl, and Joseph A. Konstan. 2006. Being Accurate is Not Enough: How Accuracy Metrics Have Hurt Recommender Systems. In CHI '06 Extended Abstracts on Human Factors in Computing Systems (CHIEA '06). ACM, New York, NY, USA, 1097--1101.
[23]
Phong Nguyen, John Dines, and Jan Krasnodebski. 2017. A Multi-Objective Learning to re-Rank Approach to Optimize Online Marketplaces for Multiple Stakeholders. CoRR abs/1708.00651 (2017).
[24]
Changhua Pei, Xinru Yang, Qing Cui, Xiao Lin, Fei Sun, Peng Jiang, Wenwu Ou, and Yongfeng Zhang. 2019. Value-aware Recommendation based on Reinforcement Profit Maximization (WWW'19).
[25]
R. Penrose. 1956. On Best Approximate Solutions of Linear Matrix Equations. Proceedings of the Cambridge Philosophical Society 52, 1 (1956), 17--19.
[26]
Filip Radlinski, Andrei Broder, Peter Ciccolo, Evgeniy Gabrilovich, Vanja Josifovski, and Lance Riedel. 2008. Optimizing Relevance and Revenue in Ad Search: A Query Substitution Approach. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '08). ACM, New York, NY, USA, 403--410.
[27]
Marco Tulio Ribeiro, Anisio Lacerda, Adriano Veloso, and Nivio Ziviani. 2012. Pareto-efficient Hybridization for Multi-objective Recommender Systems. In Proceedings of the Sixth ACM Conference on Recommender Systems (RecSys '12). ACM, New York, NY, USA, 19--26.
[28]
Marco Tulio Ribeiro, Nivio Ziviani, Edleno Silva De Moura, Itamar Hata, Anisio Lacerda, and Adriano Veloso. 2014. Multiobjective Pareto-Efficient Approaches for Recommender Systems. ACM Trans. Intell. Syst. Technol. 5, 4, Article 53 (Dec. 2014), 20 pages.
[29]
Mario Rodriguez, Christian Posse, and Ethan Zhang. 2012. Multiple Objective Optimization in Recommender Systems. In Proceedings of the Sixth ACM Conference on Recommender Systems (RecSys '12). ACM, New York, NY, USA, 11--18.
[30]
Rómer Rosales, Haibin Cheng, and Eren Manavoglu. 2012. Post-click Conversion Modeling and Analysis for Non-guaranteed Delivery Display Advertising. In Proceedings of the Fifth ACM International Conference on Web Search and Data Mining (WSDM '12). ACM, New York, NY, USA, 293--302.
[31]
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based Collaborative Filtering Recommendation Algorithms. In Proceedings of the 10th International Conference on World Wide Web (WWW '01). 285--295.
[32]
Ozan Sener and Vladlen Koltun. 2018. Multi-Task Learning as Multi-Objective Optimization. CoRR abs/1810.04650 (2018).
[33]
Liang Tang, Bo Long, Bee-Chung Chen, and Deepak Agarwal. 2016. An Empirical Study on Recommendation with Multiple Types of Feedback. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16). ACM, New York, NY, USA, 283--292.
[34]
Damir Vandic, Flavius Frasincar, and Uzay Kaymak. 2013. Facet Selection Algorithms for Web Product Search. In Proceedings of the 22Nd ACM International Conference on Information & Knowledge Management (CIKM '13). ACM, New York, NY, USA, 2327--2332.
[35]
Shanfeng Wang, Maoguo Gong, Haoliang Li, and Junwei Yang. 2016. Multi-objective Optimization for Long Tail Recommendation. Know.-Based Syst. 104, C (July 2016), 145--155.
[36]
Liang Wu, Diane Hu, Liangjie Hong, and Huan Liu. 2018. Turning Clicks into Purchases: Revenue Optimization for Product Search in E-Commerce. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR '18). ACM, New York, NY, USA, 365--374.
[37]
Qiang Wu, Christopher J. Burges, Krysta M. Svore, and Jianfeng Gao. 2010. Adapting Boosting for Information Retrieval Measures. Inf. Retr. 13, 3 (June 2010), 254--270.
[38]
Lin Xiao, Zhang Min, Zhang Yongfeng, Gu Zhaoquan, Liu Yiqun, and Ma Shaoping. 2017. Fairness-Aware Group Recommendation with Pareto-Efficiency. In Proceedings of the Eleventh ACM Conference on Recommender Systems (RecSys '17). ACM, New York, NY, USA, 107--115.
[39]
Jun Xu and Hang Li. 2007. AdaRank: A Boosting Algorithm for Information Retrieval. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '07). ACM, New York, NY, USA, 391--398.
[40]
Jun Yu, Sunil Mohan, Duangmanee (Pew) Putthividhya, and Weng-Keen Wong. 2014. Latent Dirichlet Allocation Based Diversified Retrieval for e-Commerce Search. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining (WSDM '14). ACM, New York, NY, USA, 463--472.
[41]
Mi Zhang and Neil Hurley. 2008. Avoiding Monotony: Improving the Diversity of Recommendation Lists. In Proceedings of the 2008 ACM Conference on Recommender Systems (RecSys '08). ACM, New York, NY, USA, 123--130.
[42]
Yongfeng Zhang, Qi Zhao, Yi Zhang, Daniel Friedman, Min Zhang, Yiqun Liu, and Shaoping Ma. 2016. Economic Recommendation with Surplus Maximization. In Proceedings of the 25th International Conference on World Wide Web (WWW '16). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 73--83.
[43]
Qi Zhao, Yongfeng Zhang, Yi Zhang, and Daniel Friedman. 2017. Multi-Product Utility Maximization for Economic Recommendation. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (WSDM '17). ACM, New York, NY, USA, 435--443.
[44]
Yunzhang Zhu, Gang Wang, Junli Yang, Dakan Wang, Jun Yan, and Zheng Chen. 2009. Revenue Optimization with Relevance Constraint in Sponsored Search. In Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising (ADKDD '09). ACM, New York, NY, USA, 55--60.
[45]
Eckart Zitzler, Marco Laumanns, and Lothar Thiele. 2001. SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report.

Cited By

View all
  • (2025)Improved object detection method for autonomous driving based on DETRFrontiers in Neurorobotics10.3389/fnbot.2024.148427618Online publication date: 20-Jan-2025
  • (2025)Gradient Deconfliction via Orthogonal Projections onto Subspaces For Multi-task LearningProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703503(204-212)Online publication date: 10-Mar-2025
  • (2025)A long-short-term feature extraction network based on soft-parameter-sharing for high-speed train bogies multi-object fault diagnosis under long-tailed distributionExpert Systems with Applications10.1016/j.eswa.2025.126409269(126409)Online publication date: Apr-2025
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 September 2019

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. learning to rank
  2. multiple obecjtive optimization
  3. pareto efficiency
  4. recommendation

Qualifiers

  • Research-article

Conference

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

Acceptance Rates

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

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)310
  • Downloads (Last 6 weeks)30
Reflects downloads up to 25 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2025)Improved object detection method for autonomous driving based on DETRFrontiers in Neurorobotics10.3389/fnbot.2024.148427618Online publication date: 20-Jan-2025
  • (2025)Gradient Deconfliction via Orthogonal Projections onto Subspaces For Multi-task LearningProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703503(204-212)Online publication date: 10-Mar-2025
  • (2025)A long-short-term feature extraction network based on soft-parameter-sharing for high-speed train bogies multi-object fault diagnosis under long-tailed distributionExpert Systems with Applications10.1016/j.eswa.2025.126409269(126409)Online publication date: Apr-2025
  • (2024)A Multi-Objective Framework for Balancing Fairness and Accuracy in Debiasing Machine Learning ModelsMachine Learning and Knowledge Extraction10.3390/make60301056:3(2130-2148)Online publication date: 20-Sep-2024
  • (2024)The development and application of a novel E-commerce recommendation system used in electric power B2B sectorFrontiers in Big Data10.3389/fdata.2024.13749807Online publication date: 31-Jul-2024
  • (2024)The Power of Linear Programming in Sponsored Listings Ranking: Evidence from Field ExperimentsSSRN Electronic Journal10.2139/ssrn.4767661Online publication date: 2024
  • (2024)Promoting Two-sided Fairness with Adaptive Weights for Providers and Customers in RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688169(918-923)Online publication date: 8-Oct-2024
  • (2024)Multi-Objective Recommendation via Multivariate Policy LearningProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688132(712-721)Online publication date: 8-Oct-2024
  • (2024)Pareto Front Approximation for Multi-Objective Session-Based Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688048(809-812)Online publication date: 8-Oct-2024
  • (2024)Multi-Task Neural Linear Bandit for Exploration in Recommender SystemsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671649(5723-5730)Online publication date: 25-Aug-2024
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media