Characterizing and Predicting Early Reviewers For Effective Product Marketing on E-Commerce Websites

Aim:

The aim of the project is to find out and predicting early reviewers review and predict the product popularity in E-commerce web site and also apply innovator based collaborative filtering to recommend the cold items to user.

Existing System:

The emergence of e-commerce websites has enabled users to publish or share purchase experiences by posting product reviews, which usually contain useful opinions, comments and feedback towards a product. As such, a majority of customers will read online reviews before making an informed purchase decision. It has been reported about 71% of global online shoppers read online reviews before purchasing a product. Product reviews, especially the early reviews (i.e., the reviews posted in the early stage of a product), have a high impact on subsequent product sales. We call the users who posted the early reviews early reviewers. Although early reviewers contribute only a small proportion of reviews, their opinions can determine the success or failure of new products and services. It is important for companies to identify early reviewers since their feedbacks can help companies to adjust marketing strategies and improve product designs, which can eventually lead to the success of their new products. existing methods relying on social network structures or communication channels are not suitable in our current problem of predicting early reviewers from online reviews.

Proposed System:

We take the initiative to study the behavior characteristics of early reviewers through their posted reviews on representative e-commerce platforms, e.g., Amazon and Yelp. We aim to conduct effective analysis and make accurate prediction on early reviewers. This problem is strongly related to the adoption of innovations. In a generalized view, review posting process can be considered as an adoption of innovations3, which is a theory that seeks to explain how, why, and at what rate new ideas and technology spread. The analysis and detection of early adopters in the diffusion of innovations have attracted much attention from the research community. Three fundamental elements of a diffusion process have been studied: attributes of an innovation, communication channels, and social network structures.

We quantitatively analyze the characteristics of early reviewers and their impact on product popularity. Our empirical analysis provides support to a series of theoretical conclusions from the sociology and economics.

We view review posting process as a multiplayer competition game and develop a embedding-based ranking model for the prediction of early reviewers. Our model can deal with the cold-start problem by incorporating side information of products.