A Survey on E-Commerce Learning to Rank

TL;DR

This paper surveys existing e-commerce learning to rank algorithms, comparing their effectiveness through experiments on real datasets, providing insights into their relative performance and highlighting the lack of comprehensive reviews in this domain.

Contribution

First extensive survey of e-commerce learning to rank algorithms including experimental comparison on real datasets.

Findings

Algorithms vary significantly in effectiveness based on query relevance.

Experimental results identify top-performing ranking methods.

Insights help improve future e-commerce search ranking systems.

Abstract

In e-commerce, ranking the search results based on users' preference is the most important task. Commercial e-commerce platforms, such as, Amazon, Alibaba, eBay, Walmart, etc. perform extensive and relentless research to perfect their search result ranking algorithms because the quality of ranking drives a user's decision to purchase or not to purchase an item, directly affecting the profitability of the e-commerce platform. In such a commercial platforms, for optimizing search result ranking numerous features are considered, which emerge from relevance, personalization, seller's reputation and paid promotion. To maintain their competitive advantage in the market, the platforms do no publish their core ranking algorithms, so it is difficult to know which of the algorithms or which of the features is the most effective for finding the most optimal search result ranking in e-commerce. No extensive surveys of ranking to rank in the e-commerce domain is also not yet published. In this work, we survey the existing e-commerce learning to rank algorithms. Besides, we also compare these algorithms based on query relevance criterion on a large real-life e-commerce dataset and provide a quantitative analysis. To the best of our knowledge this is the first such survey which include an experimental comparison among various learning to rank algorithms.