Bidding via Clustering Ads Intentions: an Efficient Search Engine Marketing System for E-commerce

• URL: http://arxiv.org/abs/2106.12700v2
• Date: Fri, 25 Jun 2021 01:27:47 GMT
• Title: Bidding via Clustering Ads Intentions: an Efficient Search Engine Marketing System for E-commerce
• Authors: Cheng Jie, Da Xu, Zigeng Wang, Lu Wang, Wei Shen
• Abstract summary: This paper introduces the end-to-end structure of the bidding system for search engine marketing for Walmart e-commerce. We exploit the natural language signals from the users' query and the contextual knowledge from the products to mitigate the sparsity issue. We analyze the online and offline performances of our approach and discuss how we find it as a production-efficient solution.
• Score: 13.601308818833301
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: With the increasing scale of search engine marketing, designing an efficient bidding system is becoming paramount for the success of e-commerce companies. The critical challenges faced by a modern industrial-level bidding system include: 1. the catalog is enormous, and the relevant bidding features are of high sparsity; 2. the large volume of bidding requests induces significant computation burden to both the offline and online serving. Leveraging extraneous user-item information proves essential to mitigate the sparsity issue, for which we exploit the natural language signals from the users' query and the contextual knowledge from the products. In particular, we extract the vector representations of ads via the Transformer model and leverage their geometric relation to building collaborative bidding predictions via clustering. The two-step procedure also significantly reduces the computation stress of bid evaluation and optimization. In this paper, we introduce the end-to-end structure of the bidding system for search engine marketing for Walmart e-commerce, which successfully handles tens of millions of bids each day. We analyze the online and offline performances of our approach and discuss how we find it as a production-efficient solution.

Related papers

• Bidding-Aware Retrieval for Multi-Stage Consistency in Online Advertising [30.108437268612438]
  Bidding-Aware Retrieval (BAR) is a model-based retrieval framework that addresses multi-stage inconsistency by incorporating ad bid value into the retrieval scoring function.<n>BAR's core innovation is Bidding-Aware Modeling, incorporating bid signals through monotonicity-constrained learning and multi-task distillation to ensure economically coherent representations.<n>Extensive offline experiments and full-scale deployment across Alibaba's display advertising platform validated BAR's efficacy.
  arXiv  Detail & Related papers  (2025-08-07T09:43:34Z)
• Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems [54.709976343045824]
  Current offline reinforcement learning (RL) methods face substantial challenges when applied to sparse advertising scenarios.<n>We propose MTORL, a novel multi-task offline RL model that targets two key objectives.<n>We employ multi-task learning to decode actions and rewards, simultaneously addressing channel recommendation and budget allocation.
  arXiv  Detail & Related papers  (2025-06-29T05:05:13Z)
• Multi-objective Aligned Bidword Generation Model for E-commerce Search Advertising [16.8420671443003]
  Retrieval systems primarily address the challenge of matching user queries with the most relevant advertisements.<n>We propose a Multi-objective aligned Bidword Generation Model (MoBGM), which is composed of a discriminator, generator, and preference alignment module.<n>Our proposed algorithm significantly outperforms the state of the art in offline and online experiments.
  arXiv  Detail & Related papers  (2025-06-04T10:57:18Z)
• BAT: Benchmark for Auto-bidding Task [67.56067222427946]
  We present an auction benchmark encompassing the two most prevalent auction formats.<n>We implement a series of robust baselines on a novel dataset.<n>This benchmark provides a user-friendly and intuitive framework for researchers and practitioners to develop and refine innovative autobidding algorithms.
  arXiv  Detail & Related papers  (2025-05-13T12:12:34Z)
• Semantic Ads Retrieval at Walmart eCommerce with Language Models Progressively Trained on Multiple Knowledge Domains [6.1008328784394]
  We present an end-to-end solution tailored to optimize the ads retrieval system on Walmart.com. Our approach is to pretrain the BERT-like classification model with product category information. It enhances the search relevance metric by up to 16% compared to a baseline DSSM-based model.
  arXiv  Detail & Related papers  (2025-02-13T09:01:34Z)
• Procurement Auctions via Approximately Optimal Submodular Optimization [53.93943270902349]
  We study procurement auctions, where an auctioneer seeks to acquire services from strategic sellers with private costs. Our goal is to design computationally efficient auctions that maximize the difference between the quality of the acquired services and the total cost of the sellers.
  arXiv  Detail & Related papers  (2024-11-20T18:06:55Z)
• A Primal-Dual Online Learning Approach for Dynamic Pricing of Sequentially Displayed Complementary Items under Sale Constraints [54.46126953873298]
  We address the problem of dynamically pricing complementary items that are sequentially displayed to customers. Coherent pricing policies for complementary items are essential because optimizing the pricing of each item individually is ineffective. We empirically evaluate our approach using synthetic settings randomly generated from real-world data, and compare its performance in terms of constraints violation and regret.
  arXiv  Detail & Related papers  (2024-07-08T09:55:31Z)
• Online Ad Procurement in Non-stationary Autobidding Worlds [10.871587311621974]
  We introduce a primal-dual algorithm for online decision making with multi-dimension decision variables, bandit feedback and long-term uncertain constraints. We show that our algorithm achieves low regret in many worlds when procurement outcomes are generated through procedures that are adversarial, adversarially corrupted, periodic, and ergodic.
  arXiv  Detail & Related papers  (2023-07-10T00:41:08Z)
• Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets [17.937079224726073]
  We study coordinated online bidding algorithms in repeated second-price auctions with budgets. We propose algorithms that guarantee every client a higher utility than the best she can get under independent bidding.
  arXiv  Detail & Related papers  (2023-06-13T11:55:04Z)
• ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest [60.841761065439414]
  At Pinterest, we build a single set of product embeddings called ItemSage to provide relevant recommendations in all shopping use cases. This approach has led to significant improvements in engagement and conversion metrics, while reducing both infrastructure and maintenance cost.
  arXiv  Detail & Related papers  (2022-05-24T02:28:58Z)
• A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising [53.636153252400945]
  We propose a general Multi-Agent reinforcement learning framework for Auto-Bidding, namely MAAB, to learn the auto-bidding strategies. Our approach outperforms several baseline methods in terms of social welfare and guarantees the ad platform's revenue.
  arXiv  Detail & Related papers  (2021-06-11T08:07:14Z)
• Personalized Embedding-based e-Commerce Recommendations at eBay [3.1236273633321416]
  We present an approach for generating personalized item recommendations in an e-commerce marketplace by learning to embed items and users in the same vector space. Data ablation is incorporated into the offline model training process to improve the robustness of the production system.
  arXiv  Detail & Related papers  (2021-02-11T17:58:51Z)
• A novel auction system for selecting advertisements in Real-Time bidding [68.8204255655161]
  Real-Time Bidding is a new Internet advertising system that has become very popular in recent years. We propose an alternative betting system with a new approach that not only considers the economic aspect but also other relevant factors for the functioning of the advertising system.
  arXiv  Detail & Related papers  (2020-10-22T18:36:41Z)
• Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising [52.3825928886714]
  We formulate the sequential advertising strategy optimization as a dynamic knapsack problem. We propose a theoretically guaranteed bilevel optimization framework, which significantly reduces the solution space of the original optimization space. To improve the exploration efficiency of reinforcement learning, we also devise an effective action space reduction approach.
  arXiv  Detail & Related papers  (2020-06-29T18:50:35Z)
• Real-Time Optimization Of Web Publisher RTB Revenues [10.908037452134302]
  This paper describes an engine to optimize web publisher revenues from second-price auctions. The engine is able to predict, for each auction, an optimal reserve price in approximately one millisecond.
  arXiv  Detail & Related papers  (2020-06-12T11:14:56Z)

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.