Research on Application of Collaborative Filtering in Electronic Commerce Recommender Systems

2011 ◽  
pp. 539-544 ◽  
Author(s):  
Wangjun Zhang
Keyword(s):  
Electronic Commerce ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Research On Application

A Hybrid Personalized Recommendation Method Based on Dynamic Bayesian Network

2011 ◽  
Vol 268-270 ◽  
pp. 1082-1085 ◽  
Author(s):  
Yu Long Ying ◽  
Quan Ping Hua
Keyword(s):  
Bayesian Network ◽  
Electronic Commerce ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Rapid Growth ◽  
Dynamic Bayesian Network ◽  
Dynamic Bayesian Networks ◽  
Personalized Recommendation ◽  
Data Sparsity

With the rapid growth and wide application of electronic commerce, lots of information comes forth to people. However, our experiences and knowledge often do not enough to process the vast amount of information. The problem of obtaining useful information becomes more and more serious. To deal with the problem, the personalized service and recommender system play a more important role in many fields and collaborative filtering is one of the most successful technologies in recommender systems. However, with the tremendous growth in the amount of items and users, most collaborative filtering algorithms suffer from data sparsity which leads to inaccuracy of recommendation. Aiming at the problem of data sparsity, a hybrid personalized recommendation method based on dynamic Bayesian networks is presented. This method uses the dynamic Bayesian network technique to fill the vacant ratings at first, and then employs the user-based collaborative filtering to produce recommendations.

A Collaborative Filtering Recommendation Algorithm Based on Product Clustering

2012 ◽  
Vol 267 ◽  
pp. 87-90
Author(s):  
Pu Wang
Keyword(s):  
Electronic Commerce ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommendation System ◽  
Information Overload ◽  
Personalized Recommendation ◽  
The Internet ◽  
Recommendation Algorithm ◽  
The Core ◽  
Intelligent Technology

E-commerce recommendation system is one of the most important and the most successful application field of information intelligent technology. Recommender systems help to overcome the problem of information overload on the Internet by providing personalized recommendations to the customers. Recommendation algorithm is the core of the recommendation system. Collaborative filtering recommendation algorithm is the personalized recommendation algorithm that is used widely in e-commerce recommendation system. Collaborative filtering has been a comprehensive approach in recommendation system. But data are always sparse. This becomes the bottleneck of collaborative filtering. Collaborative filtering is regarded as one of the most successful recommender systems within the last decade, which predicts unknown ratings by analyzing the known ratings. In this paper, an electronic commerce collaborative filtering recommendation algorithm based on product clustering is given. In this approach, the clustering of product is used to search the recommendation neighbors in the clustering centers.

A Collaborative Filtering Recommendation Algorithm Based on Bipartite Graph

2013 ◽  
Vol 756-759 ◽  
pp. 3865-3868 ◽  
Author(s):  
Dan Er Chen ◽  
Yu Long Ying
Keyword(s):  
Electronic Commerce ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Bipartite Graph ◽  
Rapid Growth ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Sparsity Problem

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.

scholarly journals Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems

2020 ◽  
Vol 10 (4) ◽  
pp. 1257 ◽  
Author(s):  
Liang Zhang ◽  
Quanshen Wei ◽  
Lei Zhang ◽  
Baojiao Wang ◽  
Wen-Hsien Ho
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Social Relationships ◽  
Two Stage ◽  
Proposed Model ◽  
The Social ◽  
Item List ◽  
Ranking Strategy ◽  
High Prediction ◽  
Optimization Mechanism

Conventional recommender systems are designed to achieve high prediction accuracy by recommending items expected to be the most relevant and interesting to users. Therefore, they tend to recommend only the most popular items. Studies agree that diversity of recommendations is as important as accuracy because it improves the customer experience by reducing monotony. However, increasing diversity reduces accuracy. Thus, a recommendation algorithm is needed to recommend less popular items while maintaining acceptable accuracy. This work proposes a two-stage collaborative filtering optimization mechanism that obtains a complete and diversified item list. The first stage of the model incorporates multiple interests to optimize neighbor selection. In addition to using conventional collaborative filtering to predict ratings by exploiting available ratings, the proposed model further considers the social relationships of the user. A novel ranking strategy is then used to rearrange the list of top-N items while maintaining accuracy by (1) rearranging the area controlled by the threshold and by (2) maximizing popularity while maintaining an acceptable reduction in accuracy. An extensive experimental evaluation performed in a real-world dataset confirmed that, for a given loss of accuracy, the proposed model achieves higher diversity compared to conventional approaches.

scholarly journals Robust Model-Based Reliability Approach to Tackle Shilling Attacks in Collaborative Filtering Recommender Systems

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 41782-41798 ◽  
Author(s):  
Santiago Alonso ◽  
Jesus Bobadilla ◽  
Fernando Ortega ◽  
Ricardo Moya
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Model Based ◽  
Robust Model

An Overview of Collaborative Filtering Recommendation Algorithm

2013 ◽  
Vol 756-759 ◽  
pp. 3899-3903
Author(s):  
Ping Sun ◽  
Zheng Yu Li ◽  
Zi Yang Han ◽  
Feng Ying Wang
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Evaluation Method ◽  
Decisive Role ◽  
Classical Solutions ◽  
Critical Problem ◽  
Recommendation Algorithm ◽  
Data Sparseness ◽  
Collaborative Filtering Algorithm ◽  
And Performance

Recommendation algorithm is the most core and key point in recommender systems, and plays a decisive role in type and performance evaluation. At present collaborative filtering recommendation not only is the most widely useful and successful recommend technology, but also is a promotion for the study of the whole recommender systems. The research on the recommender systems is coming into a focus and critical problem at home and abroad. Firstly, the latest development and research in the collaborative filtering recommendation algorithm are introduced. Secondly, the primary idea and difficulties faced with the algorithm are explained in detail. Some classical solutions are used to deal with the problems such as data sparseness, cold start and augmentability. Thirdly, the particular evaluation method of the algorithm is put forward and the developments of collaborative filtering algorithm are prospected.

Modelling trust networks using resistive circuits for trust-aware recommender systems

2016 ◽  
Vol 43 (1) ◽  
pp. 135-144 ◽  
Author(s):  
Mehdi Hosseinzadeh Aghdam ◽  
Morteza Analoui ◽  
Peyman Kabiri
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cold Start ◽  
User Preferences ◽  
Item Selection ◽  
Trust Networks ◽  
Trust Inference ◽  
Trust Information

Recommender systems have been widely used for predicting unknown ratings. Collaborative filtering as a recommendation technique uses known ratings for predicting user preferences in the item selection. However, current collaborative filtering methods cannot distinguish malicious users from unknown users. Also, they have serious drawbacks in generating ratings for cold-start users. Trust networks among recommender systems have been proved beneficial to improve the quality and number of predictions. This paper proposes an improved trust-aware recommender system that uses resistive circuits for trust inference. This method uses trust information to produce personalized recommendations. The result of evaluating the proposed method on Epinions dataset shows that this method can significantly improve the accuracy of recommender systems while not reducing the coverage of recommender systems.

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