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 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

/pdf/a-systematic-study-on-the-recommender-systems-in-the-e-24eg8g97m8.pdf

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

Objectives: In recent years, we have tremendous growth of users in Online Social Networks (OSN) such as Facebook, Google+, twitter etc. This becomes major reason for enabling web as largest market defining E-Commerce. Many companies use OSN as their sales channel as they can reduce operating cost for managing orders significantly compared with traditional channels. Also viral marketing is very popular in OSN. But the major drawback of this channel is if the company doesn’t satisfy the customer, then the same OSN can rapidly propagate a bad reputation of the company affecting the company’s business. Hence for customers it has become very important to identify and filter dishonest recommenders. So it becomes very important to recommend right items to right customers. In this survey, we aim to give a comprehensive review of research related to E-Commerce in OSN. Methods: First, we discuss web usage mining techniques for better shopping websites to satisfy customers. Also we discuss web mining techniques to find dishonest recommenders in OSN. Findings/ Improvements: Our survey explores the existing research highlights and also presents various researches in these topics. Also, we propose recommendation system which uses Semantic Web Mining process integrated with domain ontology which can be used to extract interesting patterns from, complex and heterogeneous data.

Survey on Web Mining Techniques and challenges of E-Commerce in Online Social Networks

Due to its robustness and rapid development, many major business firms have incorporated e-commerce as an indispensible part. It comprises of the linkage between the vendor, buyer and the host through computer systems. The paper briefs some of the major factors and issues concerning the E-Commerce domain. The paper is organized as follows. Section II deals with a detailed review of the key factors of E-Commerce. Issues pertaining to E-Commerce are briefed in Section III. The applications of E-Commerce are discussed in Section IV. The paper concludes in Section V.

/pdf/a-review-on-the-factors-that-have-driven-e-commerce-in-2y7xosxtc8.pdf

A Review on the Factors that have Driven E-Commerce in Modern Day Business

In the modern-era of technology, there is an immense boom of e-commerce business and e-commerce powered SAAS. It includes building robust services and good exchange with the internet. Therefore, the Recommendation Systems is one method to reorganize and restructure the overload of information on basis filtering and bifurcation methods. This article will illustrate a comprehensive Literature Survey regarding the published papers in the field of e-commerce recommendation systems, which will help the researchers and Software Engineers to do future work. We provided the varieties of traditional techniques followed to build such recommending systems and the challenges faced during creating such recommendation systems. In this review we analyzed various different recommendation algorithms and a potentially improved model is proposed. Building the Recommendation system is a challenging task, as it involves the problems of data sparsity and scalability requirements. Recommendation systems are crucial for E-commerce business, it provides personalized recommendations to the customer. Software Engineers and researchers can play a vital role in building and improving such recommendation systems by understanding its traditional techniques, challenges and future directions. 

E-commerce Trend Prediction Software

This article proposes a framework of Web mining- based recommender systems for e-commerce. Building on clustering analysis of data involving Web usage, content and structure, the author demonstrates how to provide users with effective recommender services according to the mining results obtained by recommender engine. Finally, the author reaches his conclusion of the advantages and practicalities of Web mining-based recommender systems for e-commerce. Keywords-E-commerce, Clustering, Recommender Systems, and Personalized Services I. INTRODUCTION As the Internet and E-commerce grows popular, E- commerce systems provide users with vast commodity information and increasing choices. Meanwhile, lost in such vast information, users often find it difficult to locate satisfactory items, for e-commerce systems cannot offer one- to-one recommendation like a salesperson. Recommender systems for e-commerce can simulate salespersons to communicate directly with users and help them find satisfactory commodities, successfully getting browsers into buying, effectively developing user relationships and increasing sales volume. Currently, almost all the large-scale e-commerce systems, such as that of Amazon, eBay, Dangdang and CDNOW, use various recommender systems in different degrees to provide their users with personalized services. Thanks to great progress in theory and practice, successful recommender systems for e- commerce will create enormous economic benefits.

/pdf/an-analysis-of-web-mining-based-recommender-systems-for-e-1sqa1hiqrf.pdf

An Analysis of Web Mining-based Recommender Systems for E-commerce

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An Analysis of Web Mining-based Recommender Systems for E-commerce