A Systematic Study on the Recommender Systems in the E-Commerce

Abstract

Electronic commerce or e-commerce includes the service and good exchange through electronic support like the Internet. It plays a crucial role in today's business and users' experience. Also, e-commerce platforms produce a vast amount of information. So, Recommender Systems (RSs) are a solution to overcome the information overload problem. They provide personalized recommendations to improve user satisfaction. The present article illustrates a comprehensive and Systematic Literature Review (SLR) regarding the papers published in the field of the e-commerce recommender systems. We reviewed the selected papers to identify the gaps and significant issues of the RSs' traditional methods, which guide the researchers to do future work. So, we provided the traditional techniques, challenges, and open issues concerning traditional methods of the field of review based on the selected papers. This review includes five categories of the RSs' algorithms, including Content-Based Filtering (CBF), Collaborative Filtering (CF), Demographic-Based Filtering (DBF), hybrid filtering, and Knowledge-Based Filtering (KBF). Also, the salient points of each selected paper are briefly reported. The publication time of the selected papers ranged from 2008 to 2019. Also, we provided a comparison table of important issues of the selected papers as well as the tables of advantages and disadvantages. Moreover, we provided a comparative table of metrics and review issues for the selected papers. And finally, the conclusions can, to a great extent, provide valuable guidelines for future studies.

Methodical Aspects of MCDM Based E-Commerce Recommender System

“…As a result, RSs minimize customer search costs and suggest products based on explicit and implicit feedback about customer behavior and preferences [27]. Recommendation systems increase turnover by offering customized products and services to the client, suggesting additional products, helping to improve customer loyalty [28]. In e-commerce, recommendation accuracy leads to increased sales and customer loyalty.…”

Dropout Graph Product for Improved Relationship Discovery Across Multiple Heterogeneous Graphs

“…Relationship discovery over graphs aims at estimating the likelihood of a future relationship between node pairs based on the observed graphs. It is at the core of many applications such as recommendation systems [1]- [4], social network analysis [5]- [8], natural language processing [9]- [12], knowledge graph construction [13]- [15], heterogeneous information networks [16], [17], and biological interaction networks [18], [19]. Extensive research has been conducted on relationship discovery, which can be broadly grouped into three categories: intra-graph, inter-graph and collective relationship discovery.…”

“…Determining whether entities in one domain are related to entities in another domain is a fundamental problem that exists in a wide range of applications. An incomplete list includes recommendation systems [1]- [4], social network analysis [5]- [8], natural language processing [9]- [12], knowledge graph reconstruction [13]- [15], heterogeneous information networks [16], [17], and biological interaction networks [18], [19].…”

A Novel Many-Objective Recommendation Algorithm for Multistakeholders

“…Recommender systems (RS) have been successfully applied to assist decision making by producing a list of items tailored to user preferences and tastes, supporting ecommerce, social media, and other applications where the content volume would otherwise be overwhelming for users [1], [2]. They have become indispensable tools of the Internet age.…”

“…Taking two chromosomes as an example, chromosome 1 is[1,5,7,9,6,12,2,1,5,4,3,7,8,1,2], and chromosome 2 is[5,7,4,1,2,10,6,1,3,7,4,3,2,8,9]. Assume this is a top-5 recommendation from three users, and the number of cut points is 2.…”