A Graph-Based Recommender System for Food Products

Abstract

In this paper we present a graph-based recommender system that is not relying on explicit item ratings to generate recommendations. Instead, it employs neighborhood-based and graph mining techniques to generate item profiles using their reviews' text. The proposed algorithm uses user review feedback to find products related to each other and tries to find a balance between similar products and highly popular products. It achieves this balance by ranking the products based on the similarity to the target product as well as its connectivity among similar products. The algorithm breaks the entire dataset into subgroups of similar products, which makes the proposed algorithm scalable as well. We present a proof-of-concept implementation of the proposed algorithm in the food product domain and present some preliminary results.