Improving Item Searching On Trading Platform Based On Reinforcement Learning Approach

Abstract

Item searching on trading platforms is a real challenge nowadays. The number of product offers on the trading platforms is significantly more than real goods. It increases the searching space for a customer and complicates the procedure of a product choosing. Often customers don't know for sure which particular sample of the product they need. They compare specific features among similar products, chose the item, and then compare pricing and shipping. For simplifying the buying process in the e-commerce market we propose to combine similar product offers from different sellers into groups and provide customers with groups of similar items. We propose an approach, which allows grouping product offers based on the pre-trained core of tags and reinforcement learning technique. The core of tags is built for each group of similar items by processing text descriptions of similar items. The suggested model builds a search query by combining words from the core of tags in order to receive the relevant list of similar items and propose a reference item of the group. As experiments have shown similar products from the e-commerce platform can be easily found if the core of tags for a group is known. The successful results significantly depend on the e-commerce platform, where the core of tags was obtained. It can significantly reduce the search space and alleviate the process of choosing a commodity.