This project was worked on as part of Stanford’s CS 224W: Analysis of Networks, led by Professor Jure Leskovec. Abstract, full report, and code repo presented below.

Abstract

In this paper, we develop and experiment with a new approach to product recommendation, in which customers’ authority relative to specific product categories is quantified and used in recommendation. Given a particular product category, we define “category experts” to be customers who have purchased relevant products, left feedback on those products, and whose comments received positive feedback by others. This way, we are able to discriminate between customers “seasoned” in particular categories and enhance the process of recommending a ranked list of products within a given category to a given user. The approach does not require any external information about users (such as demographics) or specialized product features other than product category labels; it utilizes co-purchasing information and review metadata.

Project Report:Final Project Report

Github link: Personalized Product Recommendation using Customer Expertise