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
- 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) - 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.