The Evolution of Embedding Table Optimization and Multi-Epoch Training in Pinterest Ads Conversion

• URL: http://arxiv.org/abs/2505.05605v1
• Date: Thu, 08 May 2025 19:18:32 GMT
• Title: The Evolution of Embedding Table Optimization and Multi-Epoch Training in Pinterest Ads Conversion
• Authors: Andrew Qiu, Shubham Barhate, Hin Wai Lui, Runze Su, Rafael Rios Müller, Kungang Li, Ling Leng, Han Sun, Shayan Ehsani, Zhifang Liu,
• Abstract summary: Deep learning for conversion prediction has found widespread applications in online advertising.<n>These models have become more complex as they are trained to jointly predict multiple objectives such as click, add-to-cart, checkout and other conversion types.<n>In this paper, we share key learnings from the development of embedding table optimization and multi-epoch training in Pinterest Ads Conversion models.
• Score: 4.224548289918963
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning for conversion prediction has found widespread applications in online advertising. These models have become more complex as they are trained to jointly predict multiple objectives such as click, add-to-cart, checkout and other conversion types. Additionally, the capacity and performance of these models can often be increased with the use of embedding tables that encode high cardinality categorical features such as advertiser, user, campaign, and product identifiers (IDs). These embedding tables can be pre-trained, but also learned end-to-end jointly with the model to directly optimize the model objectives. Training these large tables is challenging due to: gradient sparsity, the high cardinality of the categorical features, the non-uniform distribution of IDs and the very high label sparsity. These issues make training prone to both slow convergence and overfitting after the first epoch. Previous works addressed the multi-epoch overfitting issue by using: stronger feature hashing to reduce cardinality, filtering of low frequency IDs, regularization of the embedding tables, re-initialization of the embedding tables after each epoch, etc. Some of these techniques reduce overfitting at the expense of reduced model performance if used too aggressively. In this paper, we share key learnings from the development of embedding table optimization and multi-epoch training in Pinterest Ads Conversion models. We showcase how our Sparse Optimizer speeds up convergence, and how multi-epoch overfitting varies in severity between different objectives in a multi-task model depending on label sparsity. We propose a new approach to deal with multi-epoch overfitting: the use of a frequency-adaptive learning rate on the embedding tables and compare it to embedding re-initialization. We evaluate both methods offline using an industrial large-scale production dataset.

Related papers

• Test-Time Alignment via Hypothesis Reweighting [56.71167047381817]
  Large pretrained models often struggle with underspecified tasks.<n>We propose a novel framework to address the challenge of aligning models to test-time user intent.
  arXiv  Detail & Related papers  (2024-12-11T23:02:26Z)
• TabDiff: a Mixed-type Diffusion Model for Tabular Data Generation [91.50296404732902]
  We introduce TabDiff, a joint diffusion framework that models all mixed-type distributions of tabular data in one model.<n>Our key innovation is the development of a joint continuous-time diffusion process for numerical and categorical data.<n>TabDiff achieves superior average performance over existing competitive baselines, with up to $22.5%$ improvement over the state-of-the-art model on pair-wise column correlation estimations.
  arXiv  Detail & Related papers  (2024-10-27T22:58:47Z)
• Understanding and Scaling Collaborative Filtering Optimization from the Perspective of Matrix Rank [48.02330727538905]
  Collaborative Filtering (CF) methods dominate real-world recommender systems. We study the properties of the embedding tables under different learning strategies. We propose an efficient warm-start strategy that regularizes the stable rank of the user and item embeddings.
  arXiv  Detail & Related papers  (2024-10-15T21:54:13Z)
• Enhancing Cross-Category Learning in Recommendation Systems with Multi-Layer Embedding Training [2.4862527485819186]
  Multi-layer embeddings training (MLET) trains embeddings using factorization of the embedding layer, with an inner dimension higher than the target embedding dimension. MLET consistently produces better models, especially for rare items. At constant model quality, MLET allows embedding dimension, and model size, reduction by up to 16x, and 5.8x on average.
  arXiv  Detail & Related papers  (2023-09-27T09:32:10Z)
• Improved Distribution Matching for Dataset Condensation [91.55972945798531]
  We propose a novel dataset condensation method based on distribution matching. Our simple yet effective method outperforms most previous optimization-oriented methods with much fewer computational resources.
  arXiv  Detail & Related papers  (2023-07-19T04:07:33Z)
• RanPAC: Random Projections and Pre-trained Models for Continual Learning [59.07316955610658]
  Continual learning (CL) aims to learn different tasks (such as classification) in a non-stationary data stream without forgetting old ones. We propose a concise and effective approach for CL with pre-trained models.
  arXiv  Detail & Related papers  (2023-07-05T12:49:02Z)
• Phantom Embeddings: Using Embedding Space for Model Regularization in Deep Neural Networks [12.293294756969477]
  The strength of machine learning models stems from their ability to learn complex function approximations from data. The complex models tend to memorize the training data, which results in poor regularization performance on test data. We present a novel approach to regularize the models by leveraging the information-rich latent embeddings and their high intra-class correlation.
  arXiv  Detail & Related papers  (2023-04-14T17:15:54Z)
• HyperImpute: Generalized Iterative Imputation with Automatic Model Selection [77.86861638371926]
  We propose a generalized iterative imputation framework for adaptively and automatically configuring column-wise models. We provide a concrete implementation with out-of-the-box learners, simulators, and interfaces.
  arXiv  Detail & Related papers  (2022-06-15T19:10:35Z)
• Evolving Multi-Label Fuzzy Classifier [5.53329677986653]
  Multi-label classification has attracted much attention in the machine learning community to address the problem of assigning single samples to more than one class at the same time. We propose an evolving multi-label fuzzy classifier (EFC-ML) which is able to self-adapt and self-evolve its structure with new incoming multi-label samples in an incremental, single-pass manner.
  arXiv  Detail & Related papers  (2022-03-29T08:01:03Z)
• Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach [22.958342743597044]
  We investigate the possibilities of utilizing deep learning for cardinality estimation of similarity selection. We propose a novel and generic method that can be applied to any data type and distance function.
  arXiv  Detail & Related papers  (2020-02-15T20:22:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.