A graph model for E‐commerce recommender systems

TLDR

Information overload on the Web creates challenges for customers selecting products and for businesses identifying preferences, prompting the use of various recommender systems. The study develops a graph model that offers a generic data representation to support various recommendation methods. The authors implemented the graph model with three recommendation methods—direct retrieval, association mining, and high‑degree association retrieval—using an online bookstore dataset as a test bed. Evaluation revealed that integrating product content with historical transactions improved prediction accuracy and recommendation relevance over collaborative data alone, while high‑degree association retrieval performed similarly to association mining and direct retrieval.

Abstract

Abstract Information overload on the Web has created enormous challenges to customers selecting products for online purchases and to online businesses attempting to identify customers' preferences efficiently. Various recommender systems employing different data representations and recommendation methods are currently used to address these challenges. In this research, we developed a graph model that provides a generic data representation and can support different recommendation methods. To demonstrate its usefulness and flexibility, we developed three recommendation methods: direct retrieval, association mining, and high‐degree association retrieval. We used a data set from an online bookstore as our research test‐bed. Evaluation results showed that combining product content information and historical customer transaction information achieved more accurate predictions and relevant recommendations than using only collaborative information. However, comparisons among different methods showed that high‐degree association retrieval did not perform significantly better than the association mining method or the direct retrieval method in our test‐bed.