A NoSQL Data-based Personalized Recommendation System for C2C e-Commerce
It addresses a gap in recommendation systems for C2C e-commerce, focusing on selling rather than buying, which is incremental as it adapts existing methods to a new domain.
The paper tackles the problem of recommending selling websites for users in C2C e-commerce based on item descriptions, categories, and prices, using NoSQL data and machine learning, with experimental results showing effectiveness on real-world datasets from Vietnam.
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.