A NoSQL Data-based Personalized Recommendation System for C2C e-Commerce

TL;DR

This paper presents a NoSQL data-driven machine learning approach for personalized C2C e-commerce site recommendation, focusing on suggesting suitable selling platforms based on item descriptions and user preferences.

Contribution

It introduces a novel NoSQL-based machine learning method for recommending C2C selling websites tailored to individual item details and user needs.

Findings

Effective ranking of websites based on item and user data

Demonstrated improved recommendation accuracy on real-world datasets

Applicable to C2C e-commerce platforms in Vietnam

Abstract

With the considerable development of customer-to-customer (C2C) e-commerce in the recent years, there is a big demand for an effective recommendation system that suggests suitable websites for users to sell their items with some specified needs. Nonetheless, e-commerce recommendation systems are mostly designed for business-to-customer (B2C) websites, where the systems offer the consumers the products that they might like to buy. Almost none of the related research works focus on choosing selling sites for target items. In this paper, we introduce an approach that recommends the selling websites based upon the item's description, category, and desired selling price. This approach employs NoSQL data-based machine learning techniques for building and training topic models and classification models. The trained models can then be used to rank the websites dynamically with respect to the user needs. The experimental results with real-world datasets from Vietnam C2C websites will demonstrate the effectiveness of our proposed method.