E-Commerce Personalized Recommendation

2014 ◽  
Vol 989-994 ◽  
pp. 4996-4999 ◽  
Author(s):  
Yan Zhang
Keyword(s):  
Information Retrieval ◽  
Electronic Commerce ◽  
Association Rule ◽  
Information Overload ◽  
Rapid Development ◽  
Personalized Recommendation ◽  
Application Layer ◽  
Advantages And Disadvantages ◽  
Theory Research ◽  
Content Based Filtering

With the rapid development of electronic commerce, the problem of "information overload" leads to the difficulty that user can't search the required goods effectively , personalized recommendation technology has been applied in e-commerce and popularization. By using the method of qualitative analysis of the current e-commerce site, the paper compare the information retrieval, association rule, content-based filtering and collaborative filtering four main recommendation technologies and analysis the advantages and disadvantages in the application layer, the recommendation technologies are introduced to review e-commerce research hot topic in the field of personalized recommendation, and analysis the current domestic e-commerce personalized recommendation theory research and application status, finally propose the challenges faced by e-commerce personalized recommendation domain.

The Research of E-Commerce Personalized Recommendation

2014 ◽  
Vol 556-562 ◽  
pp. 6762-6765
Author(s):  
Yan Zhang ◽  
Tao Kuang
Keyword(s):  
Information Retrieval ◽  
Electronic Commerce ◽  
Association Rule ◽  
Information Overload ◽  
Rapid Development ◽  
Personalized Recommendation ◽  
Application Layer ◽  
Advantages And Disadvantages ◽  
Theory Research ◽  
Content Based Filtering

With the rapid development of electronic commerce, the problem of "information overload" leads to the difficulty that user can't search the required goods effectively , personalized recommendation technology has been applied in e-commerce and popularization. By using the method of qualitative analysis of the current e-commerce site,the paper compare the information retrieval, association rule, content-based filtering and collaborative filtering four main recommendation technologies and analysis the advantages and disadvantages in the application layer, the recommendation technologies are introduced to review e-commerce research hot topic in the field of personalized recommendation, and analysis the current domestic e-commerce personalized recommendation theory research and application status, finally propose the challenges faced by e-commerce personalized recommendation domain.

Personalized Recommendation System Based on Association Rules Mining and Collaborative Filtering

2010 ◽  
Vol 39 ◽  
pp. 540-544 ◽  
Author(s):  
Song Jie Gong
Keyword(s):  
Collaborative Filtering ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Recommendation System ◽  
Information Overload ◽  
Personalized Recommendation ◽  
Rule Mining ◽  
Association Rules Mining ◽  
Content Based Filtering

With the rapidly growing amount of information available, the problem of information overload is always growing acute. Personalized recommendations are an effective way to get user recommendations for unseen elements within the enormous volume of information based on their preferences. The personalized recommendation system commonly used methods are content-based filtering, collaborative filtering and association rule mining. Unfortunately, each method has its drawbacks. This paper presented a personalized recommendation method combining the association rules mining and collaborative filtering. It used the association rules mining to fill the vacant where necessary. And then, the presented approach utilizes the user based collaborative filtering to produce the recommendations. The recommendation method combining association rules mining and collaborative filtering can alleviate the data sparsity problem in the recommender systems.

scholarly journals Personalized Recommendation Model of High-Quality Education Resources for College Students Based on Data Mining

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chaohua Fang ◽  
Qiuyun Lu
Keyword(s):  
Learning Process ◽  
Data Science ◽  
Recommendation System ◽  
Information Overload ◽  
Rapid Development ◽  
Learning Ability ◽  
Personalized Recommendation ◽  
Learning Resources ◽  
Learner Model ◽  
Internet Education

With the rapid development of information technology and data science, as well as the innovative concept of “Internet+” education, personalized e-learning has received widespread attention in school education and family education. The development of education informatization has led to a rapid increase in the number of online learning users and an explosion in the number of learning resources, which makes learners face the dilemma of “information overload” and “learning lost” in the learning process. In the personalized learning resource recommendation system, the most critical thing is the construction of the learner model. Currently, most learner models generally have a lack of scientific focus that they have a single method of obtaining dimensions, feature attributes, and low computational complexity. These problems may lead to disagreement between the learner’s learning ability and the difficulty of the recommended learning resources and may lead to the cognitive overload or disorientation of learners in the learning process. The purpose of this paper is to construct a learner model to support the above problems and to strongly support individual learning resources recommendation by learning the resource model which effectively reduces the problem of cold start and sparsity in the recommended process. In this paper, we analyze the behavioral data of learners in the learning process and extract three features of learner’s cognitive ability, knowledge level, and preference for learning of learner model analysis. Among them, the preference model of the learner is constructed using the ontology, and the semantic relation between the knowledge is better understood, and the interest of the student learning is discovered.

The Analysis of Recommendation Method in EC Based on Interest Association Rule

2013 ◽  
Vol 291-294 ◽  
pp. 2798-2801 ◽  
Author(s):  
Yu Zhen Wang
Keyword(s):  
Electronic Commerce ◽  
Business Process ◽  
Association Rule ◽  
High Efficiency ◽  
Low Cost ◽  
Personalized Recommendation ◽  
Good Effect ◽  
Related Information

Electronic commerce is growing rapidly in popularity because of its high efficiency, convenience and low cost. However, E-businesses need more in-time and correct related information about customer in order to provide the service of specific aim on business process. The service of specific aim is very important for e-business to attract new customer and maintain steady customer. The recommendation method based on interest association rule in EC proposed in this thesis can predict the interest of customer according to the analysis the customer’s interest to do personalized recommendation. The recommendation method can improve recommendation level with the clear aim to get good effect.

scholarly journals A Probability-Based Hybrid User Model for Recommendation System

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Jia Hao ◽  
Yan Yan ◽  
Guoxin Wang ◽  
Lin Gong ◽  
Bo Zhao
Keyword(s):  
Product Design ◽  
Recommendation System ◽  
Information Overload ◽  
Rapid Development ◽  
User Model ◽  
Information Communication ◽  
Design Cycle ◽  
Available Information ◽  
User Ratings ◽  
Content Based Filtering

With the rapid development of information communication technology, the available information or knowledge is exponentially increased, and this causes the well-known information overload phenomenon. This problem is more serious in product design corporations because over half of the valuable design time is consumed in knowledge acquisition, which highly extends the design cycle and weakens the competitiveness. Therefore, the recommender systems become very important in the domain of product domain. This research presents a probability-based hybrid user model, which is a combination of collaborative filtering and content-based filtering. This hybrid model utilizes user ratings and item topics or classes, which are available in the domain of product design, to predict the knowledge requirement. The comprehensive analysis of the experimental results shows that the proposed method gains better performance in most of the parameter settings. This work contributes a probability-based method to the community for implement recommender system when only user ratings and item topics are available.

Research on Item Model in Content-Based Filtering Recommender Systems

2011 ◽  
Vol 480-481 ◽  
pp. 1235-1239 ◽  
Author(s):  
Song Jie Gong
Keyword(s):  
Recommender Systems ◽  
Coming Out ◽  
Information Overload ◽  
Probability Model ◽  
Information Filtering ◽  
Software Tool ◽  
Model Representation ◽  
Space Model ◽  
Advantages And Disadvantages ◽  
Content Based Filtering

With the popularization of the Internet and the development of E-commerce, the information on the Networks has increased greatly and the E-Commerce system’s structure becomes more complicated when it provides more and more choices for users. People all have experienced the feeling of being overwhelmed by the number of new books, articles, and movies coming out each year. Many researchers pay more attention on building a proper tool which can help users obtain personalized resources. Personalized recommender systems are one such software tool in which information retrieve, information filtering, and content-based filtering techniques are used to help users obtain recommendations for unseen items based on their preferences. In this paper, described item models in content-based filtering recommender systems in order to alleviate the information overload issues. The paper presented three item models as following: vector space model representation, probability model representation and improved probabilistic model representation. These item models have their own advantages and disadvantages, and can choose according to specific circumstances.

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.

scholarly journals Study on the 4P in the Innovation Strategy of Three Squirrels’ Network Marketing

2019 ◽  
Vol 1 (1) ◽  
pp. 23
Author(s):  
Wei Bi ◽  
Jie De Wu ◽  
Yang Gao ◽  
Rung Tai Lin
Keyword(s):  
Electronic Commerce ◽  
Rapid Development ◽  
Reference Value ◽  
Innovation Strategy ◽  
Market Demand ◽  
Network Marketing ◽  
Healthy Development ◽  
Marketing Innovation ◽  
Marketing Mode ◽  
Advantages And Disadvantages

<p><em>Along with the rapid development of our electronic commerce in our country, the only traditional marketing mode has been hard to meet the current market demand and it must be imperative to have vast application of marketing innovation strategy. It has been accepted and recognized by more and more people that there is an innovation strategy of network marketing centered with the marketing to improve user brands marketing; what’s more, it has been thought and applied by all kinds of major electronic commerce. Based on the mature theory of innovation strategy of network marketing, the paper would begin from the aspect of improving user brand innovation marketing to have study and analysis of the case of 4P in the innovation strategy of Three Squirrels’ Network Marketing. The successful experience and the currently existed main problem would be analyzed, and some proper suggestions would be mentioned in the paper. The paper would focus on the 4P ideas to analyze the advantages and disadvantages of Three Squirrels to explore the main experience and the existed problems. There would be correspondent improvement innovation strategy combined with the condition of enterprises so that there would be certain reference value for the improvement of Three Squirrels in the network marketing; at the same time, through the ceaseless learning and study, there would be advanced marketing idea and marketing innovation strategy at home and abroad and these could be used by other enterprises in the future so as to have a better promotion to the healthy development of enterprises.</em></p>

scholarly journals Research of the Context Recommendation Algorithm Based on the Tripartite Graph Model in Complex Systems

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Fei Long
Keyword(s):  
Complex Systems ◽  
Information Overload ◽  
Rapid Development ◽  
Three Dimensional ◽  
Graph Model ◽  
Personalized Recommendation ◽  
Three Dimensional Model ◽  
Recommendation Algorithm ◽  
Tripartite Graph ◽  
Recommendation Algorithms

With the rapid development of information technology, the information overload has become a very serious problem in web information environment. The personalized recommendation came into being. Current recommending algorithms, however, are facing a series of challenges. To solve the problem of the complex context, a new context recommendation algorithm based on the tripartite graph model is proposed for the three-dimensional model in complex systems. Improving the accuracy of the recommendation by the material diffusion, through the heat conduction to improve the diversity of the recommended objects, and balancing the accuracy and diversity through the integration of resources thus realize the personalized recommendation. The experimental results show that the proposed context recommendation algorithm based on the tripartite graph model is superior to other traditional recommendation algorithms in recommendation performance.

scholarly journals A basic framework for privacy protection in personalized information retrieval

2021 ◽  
Vol 33 (6) ◽  
pp. 0-0
Keyword(s):  
Information Retrieval ◽  
Information Overload ◽  
Rapid Development ◽  
User Privacy ◽  
Network Servers ◽  
Basic Framework ◽  
User Request ◽  
Privacy Model ◽  
Network Technologies ◽  
Cover Up

Personalized information retrieval is an effective tool to solve the problem of information overload. Along with the rapid development of emerging network technologies such as cloud computing, however, network servers are becoming more and more untrusted, resulting in a serious threat to user privacy of personalized information retrieval. In this paper, we propose a basic framework for the comprehensive protection of all kinds of user privacy in personalized information retrieval. Its basic idea is to construct and submit a group of well-designed dummy requests together with each user request to the server, to mix up the user requests and then cover up the user privacy behind the requests. Also, the framework includes a privacy model and its implementation algorithm. Finally, theoretical analysis and experimental evaluation demonstrate that the framework can comprehensively improve the security of all kinds of user privacy, without compromising the availability of personalized information retrieval.

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