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.

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.

scholarly journals Adaptive Learning Recommendation Strategy Based on Deep Q-learning

2019 ◽  
Vol 44 (4) ◽  
pp. 251-266 ◽  
Author(s):  
Chunxi Tan ◽  
Ruijian Han ◽  
Rougang Ye ◽  
Kani Chen
Keyword(s):  
Adaptive Learning ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Early Stopping ◽  
Q Learning ◽  
E Learning ◽  
Learning Scenarios ◽  
Efficient Learning ◽  
Recommendation Strategy ◽  
Full Utilization

Personalized recommendation system has been widely adopted in E-learning field that is adaptive to each learner’s own learning pace. With full utilization of learning behavior data, psychometric assessment models keep track of the learner’s proficiency on knowledge points, and then, the well-designed recommendation strategy selects a sequence of actions to meet the objective of maximizing learner’s learning efficiency. This article proposes a novel adaptive recommendation strategy under the framework of reinforcement learning. The proposed strategy is realized by the deep Q-learning algorithms, which are the techniques that contributed to the success of AlphaGo Zero to achieve the super-human level in playing the game of go. The proposed algorithm incorporates an early stopping to account for the possibility that learners may choose to stop learning. It can properly deal with missing data and can handle more individual-specific features for better recommendations. The recommendation strategy guides individual learners with efficient learning paths that vary from person to person. The authors showcase concrete examples with numeric analysis of substantive learning scenarios to further demonstrate the power of the proposed method.

SMEs ECT Reality

2009 ◽  
pp. 421-439
Author(s):  
Zakia A. Elsammani
Keyword(s):  
Strategic Planning ◽  
Electronic Commerce ◽  
Management Practices ◽  
Change Agent ◽  
Structured Interviews ◽  
Web Presence ◽  
The Difference ◽  
The Uk ◽  
Development And Management ◽  
Over Time

Lack of strategic planning in e-commerce and subsequently e-business adoption within smallto medium-sized enterprises (SMEs) has been strongly reported in literature. This chapter presents SMEs’ Web presence implementation patterns and unravels the reasons behind the lack of strategic planning when adopting Electronic Commerce Technologies (ECT). The chapter presents findings from semi-structured interviews from 11 SMEs in the Northwest of the UK. Findings reflect the difference in development and management practices of Web presence, between the more able Need Pull SMEs that identified the need to adopt ECT, and the less able Technology Push SMEs that were mostly influenced by change agent diffusion and awareness efforts. Over time, each group of SMEs reflect a different pattern in ECT implementation. This chapter depicts the issues that hinder SMEs, particularly in micro and small, in moving beyond Web site adoption.

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.

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