Personalized Intelligent Recommendation System for Electronic Commerce Based on Multi-dimensional Commodity Attributes

2022 ◽  
pp. 1259-1266
Author(s):  
Ganglong Fan ◽  
Jian Shen
Keyword(s):  
Electronic Commerce ◽  
Recommendation System

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

2013 ◽  
Vol 718-720 ◽  
pp. 1961-1966
Author(s):  
Hong Sheng Xu ◽  
Qing Tan
Keyword(s):  
Neural Network ◽  
Electronic Commerce ◽  
Bp Neural Network ◽  
Optimization Problem ◽  
Recommendation System ◽  
High Efficiency ◽  
Ontology Learning ◽  
The Neural Network ◽  
Network Process ◽  
Hidden Layer

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.

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

2014 ◽  
Vol 978 ◽  
pp. 244-247 ◽  
Author(s):  
Yi Wang ◽  
Hao Yuan Ou ◽  
Jian Ming Zhang
Keyword(s):  
Customer Satisfaction ◽  
Electronic Commerce ◽  
Customer Relationship Management ◽  
Recommender System ◽  
Recommendation System ◽  
Effective Means ◽  
Relationship Management ◽  
Customer Relationship ◽  
Semantic Structure ◽  
Design And Implementation

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.

An Electronic Commerce Recommender System Based on Product Character

2011 ◽  
Vol 267 ◽  
pp. 909-912 ◽  
Author(s):  
Shen Bao Chen
Keyword(s):  
Electronic Commerce ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Competitive Environment ◽  
Referral Process ◽  
Personalized Service ◽  
Filtering Algorithm ◽  
Customer Churn ◽  
Collaborative Filtering Algorithm

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.

Commodity Recommendation System of E-Commerce Based on Classification Users

2014 ◽  
Vol 556-562 ◽  
pp. 6689-6692 ◽  
Author(s):  
Jie Chen
Keyword(s):  
Electronic Commerce ◽  
Recommendation System ◽  
User Needs ◽  
Single Strategy ◽  
The Difference ◽  
Recommendation Strategy

With the development of electronic commerce, commodity recommendation system of using a single strategy for all users has been unable to meet the needs of businesses and consumers. This paper proposed a commodity recommendation strategy of e-commerce based on classification users, e-commerce users are classified as new users, general users, Silver users and Gold users. According to the difference of users, the paper recommended using different methods to improve the efficiency of commodity recommendation system to better meet user needs.

scholarly journals Research and Implementation of Electronic Commerce Intelligent Recommendation System Based on the Fuzzy Rough Set and Improved Cellular Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Bo Peng
Keyword(s):  
Electronic Commerce ◽  
Rough Set ◽  
Recommendation System ◽  
Information Overload ◽  
Fuzzy Rough Set ◽  
Practical Applications ◽  
Advantages And Disadvantages ◽  
The Status ◽  
The Difference ◽  
Browsing Behavior

With the continuous development of e-commerce, our society has transitioned from a mechanical era to an intelligent era. There have been many things that have subverted people’s traditional concepts, and they have also completely changed the way of life of modern people. Due to the development of e-commerce, people can enjoy the scenery and food from all over the world at home. Online shopping and online ticket purchase have greatly facilitated people’s lives and given people more choices. However, due to the excessive selection of things, there is also a phenomenon of information overload. Sometimes, it is difficult for people to find a product or content that they are very satisfied with. So, how to analyze people’s browsing behavior and predict what kind of content people want and how to push products on major websites have become a major issue facing major online companies. Based on this, this paper proposes an e-commerce intelligent recommendation technology based on the fuzzy rough set and improved cellular algorithm. It provides personalized recommendations for users based on their browsing history and purchase history. The research of this article is mainly divided into four parts. The first part is to analyze the status quo of technical research in this field. By analyzing the shortcomings of the existing technology, the concept of this article is proposed. The second part introduces the classic intelligent recommendation algorithm, including the principle and process of the fuzzy rough set and improved honeycomb algorithm, and analyzes the difference of various recommendation algorithms to illustrate the adaptability of each algorithm in practical applications and their respective advantages and disadvantages. The third part introduces an intelligent recommendation system based on fuzzy clustering, comprehensively analyzes the characteristics of users and commodities, makes full use of users’ evaluation information of commodities, and realizes intelligent recommendation based on content and collaborative filtering. At the end of the article, through comparative analysis experiments, the superiority of the intelligent recommendation system for electronic commerce based on the fuzzy rough set and improved cellular algorithm is further proved, and the accuracy of intelligent recommendation is improved.

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

2014 ◽  
Vol 543-547 ◽  
pp. 3674-3677
Author(s):  
Yu Xia Wang
Keyword(s):  
Electronic Commerce ◽  
Recommendation System ◽  
Hot Spot ◽  
Low Cost ◽  
Route Planning ◽  
User Interaction ◽  
Developed Countries ◽  
Tourism Industry ◽  
Business In China ◽  
Industry Planning

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

2014 ◽  
Vol 513-517 ◽  
pp. 643-646
Author(s):  
Ping Su
Keyword(s):  
Electronic Commerce ◽  
Recommender Systems ◽  
Bayesian Model ◽  
Recommendation System ◽  
Poor Quality ◽  
Time And Space ◽  
Personalized Services

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.

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