research-article

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

Authors:

Yongfeng Zhang,

Peng JiangAuthors Info & Claims

RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems

Pages 20 - 28

https://doi.org/10.1145/3298689.3346998

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.

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Index Terms

A pareto-efficient algorithm for multiple objective optimization in e-commerce recommendation
1. Computing methodologies
  1. Symbolic and algebraic manipulation
    1. Symbolic and algebraic algorithms
      1. Optimization algorithms
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Learning to rank
    2. Retrieval tasks and goals
      1. Recommender systems

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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

General Chairs:
Toine Bogers
Aalborg University Copenhagen, Denmark
,
Alan Said
University of Gothenburg, Sweden
,
Program Chairs:
Peter Brusilovsky
University of Pittsburgh
,
Domonkos Tikk
Gravity R&D, Hungary

Copyright © 2019 ACM.

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Published: 10 September 2019

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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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Zhu SZhao HWu TWang PDeng HXu JZheng BNejdl WAuer SCha MMoens MNajork M(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
https://dl.acm.org/doi/10.1145/3701551.3703503
Liu YChen JPan TXie J(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
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Nagpal RKhan ABorkar MGupta A(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
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