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Exploration in Recommender Systems

Published: 15 February 2022 Publication History

Abstract

In the era of increasing choices, recommender systems are becoming indispensable in helping users navigate the million or billion pieces of content on recommendation platforms. Most of the recommender systems are powered by ML models trained on a large amount of user-item interaction data. Such a setup however induces a strong feedback loop that creates the rich gets richer phenomenon where head contents are getting more and more exposure while tail and fresh contents are not discovered. At the same time, it pigeonholes users to contents they are already familiar with. We believe exploration is key to break away from the feedback loop and to optimize long term user experience on recommendation platforms.
The exploration-exploitation tradeoff, being the foundation of bandits and RL research, has been extensively studied in RL. While effective exploration is believed to positively influence the user experience on the platform, the exact value of exploration in recommender systems has not been well established. In this talk, we examine the roles of exploration in recommender systems in three facets: 1) system exploration to surface fresh/tail recommendations based on users' known interests; 2) user exploration to identify unknown user interests or introduce users to new interests; and 3) online exploration to utilize real-time user feedback to reduce extrapolation errors in performing system and user exploration. We discuss the challenges in measurements and optimization in different types of exploration, and propose initial solutions. We showcase how each aspect of exploration contributes to the long term user experience through offline and live experiments on industrial recommendation platforms. We hope this talk can inspire more follow up work in understanding and improving exploration in recommender systems.

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  • (2023)Navigating the Feedback Loop in Recommender Systems: Insights and Strategies from Industry PracticeProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3610246(1058-1061)Online publication date: 14-Sep-2023

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cover image ACM Conferences
WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
February 2022
1690 pages
ISBN:9781450391320
DOI:10.1145/3488560
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: 15 February 2022

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Author Tags

  1. bandits
  2. exploration
  3. online learning
  4. recommender systems
  5. reinforcement learning
  6. serendipity
  7. uncertainty

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WSDM '22

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Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

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  • (2023)Navigating the Feedback Loop in Recommender Systems: Insights and Strategies from Industry PracticeProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3610246(1058-1061)Online publication date: 14-Sep-2023

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