search
HomeTechnology peripheralsAIeBay uses machine learning to improve sale listings

​Translator | Bugatti

Reviewer | Sun Shujuan

The online marketplace eBay has added additional buying signals to its machine learning model, such as “add to watchlist”, “ Bid" and "Add to Cart" to increase the relevance of recommended ad listings based on the initial product being searched. Chen Xue gave a very detailed introduction in this recent article​​.

eBay uses machine learning to improve sale listings

# eBay’s Promotional Listing Standard (PLS) is a paid option for sellers. Using the PLSIM option, eBay's recommendation engine will recommend sponsored products similar to the one the potential buyer just clicked on. PLSIM pays on a CPA model (sellers only pay eBay when a sale is made), so this is a great incentive to create the most efficient model to promote the best listings. This often works out for sellers, buyers, and eBay.

The PLSIM journey is as follows:

1. User searches for products.

2. The user clicks on the results from the search -> Log in to View Items (VI) page to view the listed items (eBay calls them seed items).

3. Users scroll down the VI page and can see recommended products in PLSIM.

4. The user clicks on the product from PLSIM, performs an action (view, add to shopping cart, buy now, etc.), or view another new set of recommended products.

eBay uses machine learning to improve sale listings

From a machine learning perspective, the PLSIM journey is as follows:

    Retrieve the subset candidate promotion list criteria that are most closely related to the seed item ("Check the complete collection").
  1. Use a trained machine learning sorter to sort the product list in the search set according to the likelihood of purchase.
  2. Reorder the product list based on advertising rates to balance seller sales speed achieved through promotions with recommendation relevance.
Ranking model

The ranking model is based on the following historical data:

    Data of recommended products
  • Recommended products similar to the seed product
  • Context (Country and Product Category)
  • User Personalization Features
eBay uses gradient boosting trees that, for a specific seed item, Sort items based on their relative purchase probability.

From binary feedback to multiple correlation feedback

In the past, purchase probability relied on binary purchase data. It is "relevant" if it is purchased with the seed item, otherwise it is "irrelevant". This is a failed approach, but there are several major areas where it can be optimized:

    False Negatives: Since users typically only purchase one item from the recommended list, the purchase does not occur when the purchase is not made. In some cases, good recommendations may be viewed as bad recommendations, leading to false positives.
  • Few purchases: Compared to other user events, it is becoming challenging to train a model with sufficient number and diversity of purchases to predict the forward class.
  • Missing data: From clicks to add to cart, numerous user actions reveal a wealth of user information and reveal possible outcomes.
To summarize, eBay engineers consider the following user actions, in addition to initial clicks and how to add them to the ranking model:

    Buy Now (only applicable At Buy-It-Now i.e. BIN Listing)
  • Add to Cart (BIN Listing Only)
  • Bid (Best Bid Listing Only)
  • Ask for Bid (Applies to Auction Listings only)
  • Add to Watchlist (Applies to BIN, Best Bid, or Auction Listings)

eBay uses machine learning to improve sale listings

User Interface Example

Relevance Levels of Multiple Relevance Feedback

eBay now knows that purchases are extremely relevant, so it needs to add additional actions, but the new question is: where do these actions fall within the relevance hierarchy?

The image below illustrates how eBay sorts the remaining possible actions - "Bid," "Buy Now," "Add to Watchlist," and "Add to Cart."

eBay uses machine learning to improve sale listings

In the historical training data for seed items, each potential item is labeled with a relevance level by the following scale.

eBay uses machine learning to improve sale listings

Marked result is that during training, the sorter penalizes incorrectly ordered purchases more severely than incorrectly ordered "Buy Now" , and so on.

Sample weights for multiple correlation feedback

Gradient boosted trees support multiple labels to capture a range of correlations, but there is no direct way to achieve the magnitude of the correlation.

eBay had to run the tests iteratively until it came up with numbers that made the model work. The researchers added additional weights (called "sample weights") that were fed into the pairwise loss function. They optimized the hyperparameter tuning and ran it for 25 iterations before arriving at the best sample weights - "Add to Watchlist" (6), "Add to Cart" (15), "Bid" (38 ), "Buy Now" (8) and "Buy" (15). Without sample weights, the new model will perform worse. With sample weights, the new model outperforms the binary model.

They tried adding only clicks as additional relevant feedback and applied the tuned hyperparameter "Purchase" sample weight of 150. Offline results are also shown below, where "BOWC" stands for Buy Now, Make a Bid, Add to Watchlist, and Add to Cart actions. Purchase ranking reflects the average ranking of items purchased. The smaller the better.

eBay uses machine learning to improve sale listings

Conclusion

The trained model has a total of more than 2000 instances. A/B testing is conducted in two stages. The first phase, which only included additional select tags and showed a 2.97% increase in purchase volume on the eBay mobile app and a 2.66% increase in ad revenue, was deemed successful enough to move the model into global production.

The second phase added more actions to the model, such as "Add to Watchlist", "Add to Cart", "Bid" and "Buy Now", and A/B testing showed better customer engagement (e.g. more clicks and BWC).

eBay uses machine learning to improve sale listings

Original title: EBay Uses Machine Learning to Refine Promoted Listings​, Author: Jessica Wachtel​

The above is the detailed content of eBay uses machine learning to improve sale listings. For more information, please follow other related articles on the PHP Chinese website!

Statement
This article is reproduced at:51CTO.COM. If there is any infringement, please contact admin@php.cn delete
MarkItDown MCP Can Convert Any Document into Markdowns!MarkItDown MCP Can Convert Any Document into Markdowns!Apr 27, 2025 am 09:47 AM

Handling documents is no longer just about opening files in your AI projects, it’s about transforming chaos into clarity. Docs such as PDFs, PowerPoints, and Word flood our workflows in every shape and size. Retrieving structured

How to Use Google ADK for Building Agents? - Analytics VidhyaHow to Use Google ADK for Building Agents? - Analytics VidhyaApr 27, 2025 am 09:42 AM

Harness the power of Google's Agent Development Kit (ADK) to create intelligent agents with real-world capabilities! This tutorial guides you through building conversational agents using ADK, supporting various language models like Gemini and GPT. W

Use of SLM over LLM for Effective Problem Solving - Analytics VidhyaUse of SLM over LLM for Effective Problem Solving - Analytics VidhyaApr 27, 2025 am 09:27 AM

summary: Small Language Model (SLM) is designed for efficiency. They are better than the Large Language Model (LLM) in resource-deficient, real-time and privacy-sensitive environments. Best for focus-based tasks, especially where domain specificity, controllability, and interpretability are more important than general knowledge or creativity. SLMs are not a replacement for LLMs, but they are ideal when precision, speed and cost-effectiveness are critical. Technology helps us achieve more with fewer resources. It has always been a promoter, not a driver. From the steam engine era to the Internet bubble era, the power of technology lies in the extent to which it helps us solve problems. Artificial intelligence (AI) and more recently generative AI are no exception

How to Use Google Gemini Models for Computer Vision Tasks? - Analytics VidhyaHow to Use Google Gemini Models for Computer Vision Tasks? - Analytics VidhyaApr 27, 2025 am 09:26 AM

Harness the Power of Google Gemini for Computer Vision: A Comprehensive Guide Google Gemini, a leading AI chatbot, extends its capabilities beyond conversation to encompass powerful computer vision functionalities. This guide details how to utilize

Gemini 2.0 Flash vs o4-mini: Can Google Do Better Than OpenAI?Gemini 2.0 Flash vs o4-mini: Can Google Do Better Than OpenAI?Apr 27, 2025 am 09:20 AM

The AI landscape of 2025 is electrifying with the arrival of Google's Gemini 2.0 Flash and OpenAI's o4-mini. These cutting-edge models, launched weeks apart, boast comparable advanced features and impressive benchmark scores. This in-depth compariso

How to Generate and Edit Images Using OpenAI gpt-image-1 APIHow to Generate and Edit Images Using OpenAI gpt-image-1 APIApr 27, 2025 am 09:16 AM

OpenAI's latest multimodal model, gpt-image-1, revolutionizes image generation within ChatGPT and via its API. This article explores its features, usage, and applications. Table of Contents Understanding gpt-image-1 Key Capabilities of gpt-image-1

How to Perform Data Preprocessing Using Cleanlab? - Analytics VidhyaHow to Perform Data Preprocessing Using Cleanlab? - Analytics VidhyaApr 27, 2025 am 09:15 AM

Data preprocessing is paramount for successful machine learning, yet real-world datasets often contain errors. Cleanlab offers an efficient solution, using its Python package to implement confident learning algorithms. It automates the detection and

The AI Skills Gap Is Slowing Down Supply ChainsThe AI Skills Gap Is Slowing Down Supply ChainsApr 26, 2025 am 11:13 AM

The term "AI-ready workforce" is frequently used, but what does it truly mean in the supply chain industry? According to Abe Eshkenazi, CEO of the Association for Supply Chain Management (ASCM), it signifies professionals capable of critic

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

MantisBT

MantisBT

Mantis is an easy-to-deploy web-based defect tracking tool designed to aid in product defect tracking. It requires PHP, MySQL and a web server. Check out our demo and hosting services.

DVWA

DVWA

Damn Vulnerable Web App (DVWA) is a PHP/MySQL web application that is very vulnerable. Its main goals are to be an aid for security professionals to test their skills and tools in a legal environment, to help web developers better understand the process of securing web applications, and to help teachers/students teach/learn in a classroom environment Web application security. The goal of DVWA is to practice some of the most common web vulnerabilities through a simple and straightforward interface, with varying degrees of difficulty. Please note that this software

mPDF

mPDF

mPDF is a PHP library that can generate PDF files from UTF-8 encoded HTML. The original author, Ian Back, wrote mPDF to output PDF files "on the fly" from his website and handle different languages. It is slower than original scripts like HTML2FPDF and produces larger files when using Unicode fonts, but supports CSS styles etc. and has a lot of enhancements. Supports almost all languages, including RTL (Arabic and Hebrew) and CJK (Chinese, Japanese and Korean). Supports nested block-level elements (such as P, DIV),

ZendStudio 13.5.1 Mac

ZendStudio 13.5.1 Mac

Powerful PHP integrated development environment