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Ads Recommendation in a Collapsed and Entangled World

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Jie JiangAuthors Info & Claims

KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 5566 - 5577

https://doi.org/10.1145/3637528.3671607

Published: 24 August 2024 Publication History

Abstract

We present Tencent's ads recommendation system and examine the challenges and practices of learning appropriate recommendation representations. Our study begins by showcasing our approaches to preserving prior knowledge when encoding features of diverse types into embedding representations. We specifically address sequence features, numeric features, and pre-trained embedding features. Subsequently, we delve into two crucial challenges related to feature representation: the dimensional collapse of embeddings and the interest entanglement across different tasks or scenarios. We propose several practical approaches to address these challenges that result in robust and disentangled recommendation representations. We then explore several training techniques to facilitate model optimization, reduce bias, and enhance exploration. Additionally, we introduce three analysis tools that enable us to study feature correlation, dimensional collapse, and interest entanglement. This work builds upon the continuous efforts of Tencent's ads recommendation team over the past decade. It summarizes general design principles and presents a series of readily applicable solutions and analysis tools. The reported performance is based on our online advertising platform, which handles hundreds of billions of requests daily and serves millions of ads to billions of users.

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Cited By

Lin ZPan JZhang SWang XXiao XHuang SXiao LJiang JBaeza-Yates RBonchi F(2024)Understanding the Ranking Loss for Recommendation with Sparse User FeedbackProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671565(5409-5418)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671565

Index Terms

Ads Recommendation in a Collapsed and Entangled World
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Factorization methods
      2. Neural networks
2. Information systems
  1. World Wide Web
    1. Online advertising
      1. Display advertising

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cover image ACM Conferences

KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2024

6901 pages

ISBN:9798400704901

DOI:10.1145/3637528

General Chairs:
Ricardo Baeza-Yates
Northeastern University, USA
,
Francesco Bonchi
CENTAI / Eurecat, Italy

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Lin ZPan JZhang SWang XXiao XHuang SXiao LJiang JBaeza-Yates RBonchi F(2024)Understanding the Ranking Loss for Recommendation with Sparse User FeedbackProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671565(5409-5418)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671565

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