scholarly journals Research on Personalized Recommendation System Based on Association Rules

2021 ◽  
Vol 1961 (1) ◽  
pp. 012027
Author(s):  
HaiYan Song ◽  
Huan Zhang ◽  
ZhiHao Xing
Keyword(s):  
Association Rules ◽  
Recommendation System ◽  
Personalized Recommendation

Research on Intelligent Recommendation Method and its Application on Internet Bookstore

2010 ◽  
Vol 121-122 ◽  
pp. 447-452
Author(s):  
Qing Zhang Chen ◽  
Yu Jie Pei ◽  
Yan Jin ◽  
Li Yan Zhang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Experimental Results ◽  
Personalized Recommendation ◽  
The Internet

As the current personalized recommendation systems of Internet bookstore are limited too much in function, this paper build a kind of Internet bookstore recommendation system based on “Strategic Data Mining”, which can provide personalized recommendations that they really want. It helps us to get the weight attribute of type of book by using AHP, the weight attributes spoken on behalf of its owner, and we add it in association rules. Then the method clusters the customer and type of book, and gives some strategies of personalized recommendation. Internet bookstore recommendation system is implemented with ASP.NET in this article. The experimental results indicate that the Internet bookstore recommendation system is feasible.

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.

Study the Personal Push Service of University Library Based on Big Data Mining

2014 ◽  
Vol 998-999 ◽  
pp. 1261-1265 ◽  
Author(s):  
Cheng Yi ◽  
Ying Xia ◽  
Zhi Yong Zhang
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Association Rules ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Data Resource ◽  
Big Data Mining ◽  
University Library ◽  
Library Resources ◽  
Classical Association

It expounds the big data and the relevant theoretical knowledge of big data mining, In view of the lack of effective analysis of the data resource access in delivery service of university library, this paper designs the personalized recommendation system service model of university library, with clustering analysis and association rules theory as the foundation of technology. And it introduces in detail how to cluster according to the user's attribute characteristics and how to introduce minimum support to opti-mize on the basis of the classical association rules algorithm. Experiments show that the improved algorithm can improves the utilization of library resources.

scholarly journals Phone-Based Restaurant Recommendation System by Using Position Information and Association Rules

2012 ◽  
Vol 6-7 ◽  
pp. 783-789
Author(s):  
Jian Feng Dong ◽  
Tian Yang Dong ◽  
Jia Jie Yao ◽  
Ling Zhang
Keyword(s):  
Mobile Phone ◽  
Real Time ◽  
Association Rules ◽  
Recommendation System ◽  
Rapid Development ◽  
Smart Phone ◽  
Smart Phones ◽  
Location Information ◽  
Position Information ◽  
The Real

With the rapid development of smart-phone applications, how to make the ordering process via smart-phones more convenient and intelligent has become a hotspot. This paper puts forward a method of restaurant dish recommendation relying on position information and association rules. In addition, this paper has designed and developed a restaurant recommendation system based on mobile phone. The system would fetch the real-time location information via smart-phones, and provide customers personalized restaurant and dish recommendation service. According to the related applications, this system can successfully recommend the related restaurants and food information to customers.

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