A Survey on E-Commerce Learning to Rank

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 a product, 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 are the most effective for finding the most optimal search result ranking in e-commerce. No extensive surveys of learning to rank (LTR) in the e-commerce domain as such is 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 comparison. To the best of our knowledge this is the first such survey which include an experimental comparison among various learning to rank algorithms.