Recommender Systems in E-commerce

E-commerce recommender systems are becoming increasingly important in the current digital world. They are used to personalize user experience, help customers find what they need quickly and efficiently, and increase revenue for the business. However, there are several challenges associated with big data-based e-commerce recommender systems. These challenges include limited resources, data validity period, cold start, long tail problem, scalability. In this paper, we discuss the challenges and potential solutions to overcome these challenges. We also discuss the different types of e-commerce recommender systems, their advantages, and disadvantages. We conclude with some future research directions to improve the performance of e-commerce recommender systems

Study on Recommender Systems for Business-To-Business Electronic Commerce

Recommender systems have become a popular technique and strategy for helping users select desirable products or services. Most research in this area focused on applying the method to help the customers in Business-to-Customer (B2C) electronic commerce (e-commerce), however, the participants in Business- to-Business (B2B) market can also get useful assistance from the recommender system. In this article we discuss the application of recommender system to B2B e-commerce. First, we examine how recommender system help B2B participants do transactions easier, then we design an effective system framework for the B2B e-commerce\u27s recommender system based on B2B business practices and business intelligence; and then, we define the model components and processes; in the end, the ongoing challenges of the application will be discussed

Promoting cold-start items in recommender systems

As one of major challenges, cold-start problem plagues nearly all recommender systems. In particular, new items will be overlooked, impeding the development of new products online. Given limited resources, how to utilize the knowledge of recommender systems and design efficient marketing strategy for new items is extremely important. In this paper, we convert this ticklish issue into a clear mathematical problem based on a bipartite network representation. Under the most widely used algorithm in real e-commerce recommender systems, so-called the item-based collaborative filtering, we show that to simply push new items to active users is not a good strategy. To our surprise, experiments on real recommender systems indicate that to connect new items with some less active users will statistically yield better performance, namely these new items will have more chance to appear in other users' recommendation lists. Further analysis suggests that the disassortative nature of recommender systems contributes to such observation. In a word, getting in-depth understanding on recommender systems could pave the way for the owners to popularize their cold-start products with low costs.Comment: 6 pages, 6 figure

A Survey of e-Commerce Recommender Systems

Due to their powerful personalization and efficiency features, recommendation systems are being used extensively in many online environments. Recommender systems provide great opportunities to businesses, therefore research on developing new recommender system techniques and methods have been receiving increasing attention. This paper reviews recent developments in recommender systems in the domain of ecommerce. The main purpose of the paper is to summarize and compare the latest improvements of e-commerce recommender systems from the perspective of e-vendors. By examining the recent publications in the field, our research provides thorough analysis of current advancements and attempts to identify the existing issues in recommender systems. Final outcomes give practitioners and researchers the necessary insights and directions on recommender systems

Applying Recommendation Techniques In Conventional Grocery Retailing

In grocery retailing, promotions and recommendations, derived from traditional data mining techniques, apply uniformly to all customers and not to individual ones, thus failing to meet each customer’s personal needs. On the other hand, recommender systems have been widely explored in the field of e-commerce managing to provide targeted personalized recommendations for products and services. Despite the great success of recommender systems in internet retailing, their application in many other fields remains practically unexplored. RFID and pervasive networking technologies now offer the potentials to utilize recommender systems in physical environment. The scope of this paper is to examine the individual characteristics of the new domain along with the applicability of various recommendations techniques. The results indicate the superiority of the e-commerce recommendation techniques against the traditional approaches currently used in grocery retailing

The Determinants of Acceptance of Recommender Systems: Applying the UTAUT Model

This study investigates how consumers assess the quality of two types of recommender systems, collaborative filtering and content-based, in the content of e-commerce by using a modified Unified Theory of Acceptance and Use of Technology (UTAUT) model. Specifically, the under-investigated concept of trust in technological artifacts is adapted to a modified UTAUT model. Additionally, this study considers hedonic and utilitarian product characteristics, attempting to present a comprehensive range of recommender system acceptance. A total of 51 participants completed an online 2 (recommender systems) x 2 (products) survey. The results suggested that type of recommender systems and products did have different impacts on the behavioral intention to use recommender systems. This study may be of importance in explaining factors contributing to use recommender systems, as well as in providing designers of recommender systems with a better understanding of how to provide a more effective recommender system