Electronic Commerce Recommendation System Research Articles

Electronic commerce recommendation mobile crowd system based on cooperative data collection and embedded control

It is known that the collection of the specific needs of mobile users and location management in an electronic commerce recommendation system are important indicators used to evaluate user satisfaction and system execution efficiency. In order to improve the low accuracy of recommendation systems and ensure real-time location management, we proposed cooperative embedded data collection and control of the electronic commerce recommendation system for mobile crowd system. First of all, we would assign every user with a mobile crowd terminal. The terminal could collect the user’s demand and personalized data. An embedded terminal could provide users with a real-time, accurate, personalized shopping experience. The terminal could collect the user demand dynamic evolution function. Then, the electronic commerce recommendation application platform would be formed through the deployment of a mobile crowd terminal. The platform collects and forwards the signal acquisition through a single port, dual port and port more adaptive network structure. Experimental results show that the performance of the proposed scheme is superior to the electronic commerce recommendation system based on user personalization drive, such as the data collection of the embedded crowd terminal delay, precision and accuracy of electronic commerce recommendation, as well as the number of iterations. The proposed scheme is more suitable for an electronic commerce recommendation system.

Design and Implementation of E-Commerce Recommendation System Based on Ontology Technology

Electronic commerce recommendation system can effectively retain customers, effective means to improve the electronic commerce system sales. This paper first analyzes the E-commerce recommender system based on ontology, and applies it to the clothing e-commerce website customer relationship management and personalized commodity recommendation; semantic structure through ontology has to commodity recommendation. The paper presents design and implementation of E-commerce recommendation system based on ontology technology so as to effectively improve customer satisfaction.

A Study on Tourism Route Planning Method Based on Association Rule Mining

In recent years, electronic commerce with the convenience and low cost and wide spread has quickly participated in the tourism industry. [This model marks the new mode of electronic commerce. Tourism is information intensive and information based on industry, leading to walk on the tourism electronic commerce. In developed countries, the development of tourism electronic business in China is still lagging behind that in the west, which is also active in the exploration stage. Hot spot in the current research on electronic commerce recommendation system mainly focus on how to improve the efficiency and accuracy of goods, through the interface and user interaction, providing recommendation for users to help users find the products. Thus completing the purchase process improves user loyalty for his website, users won more favor. Electronic commerce recommendation systems in theory and application have been greatly developed, but there are also a series of challenges and problems, while the tourism industry planning and tourism development do not take full advantage of the related technology.

Electronic Commerce Recommendation Mechanism Based on QoS and Bayesian Model

Electronic commerce recommender systems represent personalized services that want to predict users interest on information items. However, traditional recommendation system has suffered from its shortage in scalability as their calculation complexity increases quickly both in time and space when the number of the user and item in the rating database increases. Poor quality is also one challenge in electronic commerce recommender systems. The paper proposed an electronic commerce recommendation mechanism based on QoS and Bayesian model. And the proposed recommender method combining QoS and Bayesian can improve the accuracy of the electronic commerce recommendation system.

A Collaborative Filtering Recommendation Algorithm Based on Bipartite Graph

With the rapid growth and wide application of Internet, everyday there are many of information generated and the existence of a large amount of information makes it hardly to mining the wanted information. The recommendation algorithm is the process to alleviative the problem. Collaborative filtering algorithm is one successful personalized recommendation technology, and is widely used in many fields. But traditional collaborative filtering algorithm has the problem of sparsity, which will influence the efficiency of prediction. In this paper, a collaborative filtering recommendation algorithm based on bipartite graph is proposed. The algorithm takes users, items and tags into account, and also studies the degree of tags which may affect the similarity of users. The collaborative filtering recommendation algorithm based on bipartite graph can alleviate the sparsity problem in the electronic commerce recommender systems.

Building Electronic Commerce Recommendation System Based on Ontology Learning and BP Neural Network

Electronic commerce recommendation system can effectively retain user, prevent users from erosion, and improve e-commerce system sales. BP neural network using iterative operation, solving the weights of the neural network and close values to corresponding network process of learning and memory, to join the hidden layer nodes of the optimization problem of adjustable parameters increase. Ontology learning is the use of machine learning and statistical techniques, with automatic or semi-automatic way, from the existing data resources and obtaining desired body. The paper presents building electronic commerce recommendation system based on ontology learning and BP neural network. Experimental results show that the proposed algorithm has high efficiency.

A Flexible Electronic Commerce Recommendation System

Recommendation systems have become very popular in E-commerce websites. Many of the largest commerce websites are already using recommender technologies to help their customers find products to purchase. An electronic commerce recommendation system learns from a customer and recommends products that the customer will find most valuable from among the available products. But most recommendation methods are hard-wired into the system and they support only fixed recommendations. This paper presented a framework of flexible electronic commerce recommendation system. The framework is composed by user model interface, recommendation engine, recommendation strategy model, recommendation technology group, user interest model and database interface. In the recommender strategy model, the method can be collaborative filtering, content-based filtering, mining associate rules method, knowledge-based filtering method or the mixed method. The system mapped the implementation and demand through strategy model, and the whole system would be design as standard parts to adapt to the change of the recommendation strategy.

The Role of Cognitive Decision Effort in Electronic Commerce Recommendation System

The Role of Cognitive Decision Effort in Electronic Commerce Recommendation System

A Collaborative Filtering Recommendation Algorithm Based on User Clustering in E-Commerce Personalized Systems

Electronic commerce recommender systems are becoming increasingly popular with the evolution of the Internet, and collaborative filtering is the most successful technology for building recommendation systems. Unfortunately, the efficiency of this method declines linearly with the number of users and items. So, as the magnitudes of users and items grow rapidly, the result in the difficulty of the speed bottleneck of collaborative filtering systems. In order to raise service efficiency of the personalized systems, a collaborative filtering recommendation method based on clustering of users is presented. Users are clustered based on users ratings on items, then the nearest neighbors of target user can be found in the user clusters most similar to the target user. Based on the algorithm, the collaborative filtering algorithm should be divided into two stages, and it separates the procedure of recommendation into offline and online phases. In the offline phase, the basic users are clustered into centers; while in the online phase, the nearest neighbors of an active user are found according to the basic users’ cluster centers, and the recommendation to the active user is produced.

An Electronic Commerce Recommender System Based on Product Character

In the increasingly competitive environment, in order to effectively preserve the user, preventing customer churn, increase sales of e-commerce systems, e-commerce recommendation system in the importance of the products has been revealed. Recommendation system in e-commerce system can provide commodity information and advice to help customers decide what products to buy, analog sales staff to complete the purchase of goods to the customer referral process so that customers feel completely personalized service. To improve the item-based collaborative filtering algorithm, an electronic commerce recommendation system based on product character is presented. This approach revises the original similarity using product character, takes into account the influence of product character and customer rating, and combines the customer rating similarity and the product character similarity.

Research and Design of a Grid Based Electronic Commerce Recommendation System

Current electronic commerce recommendation system is designed for single electronic commerce website and current recommendation technologies have obvious deficiencies Centralized recommendation systems can not resolve the contradiction between high recommendation quality and timely response, as well as that between limited recommendation range and ever rich information on the web. Distributed recommendation systems are expected to improve the recommendation quality while maintaining high performance. This paper analyses the problems of traditional electronic commerce recommendation system and clarifies the advantage of applying grid technology into electronic commerce recommendation system. It discusses the prototype of an intelligent recommendation system, namely grid based electronic commerce recommendation system (GBECRS). The paper analyses rationale and mechanism of GBECRS and designs its logical structure. It focuses on the design of grid services which are needed to be deployed into electronic commerce recommendation grid. Finally it does deep analysis of key technologies that are applied in the system.