Improved Personalized Recommendation based on Causal Association Rule and Collaborative Filtering

There are usually limited user evaluation of resources on a recommender system, which caused an extremely sparse user rating matrix, and this greatly reduce the accuracy of personalized recommendation, especially for new users or new items. This paper presents a recommendation method based on rating...

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Bibliographic Details
Published in:International journal of distance education technologies 2016-07, Vol.14 (3), p.21-33
Main Authors: Lei, Wu, Qing, Fang, Zhou, Jin
Format: Article
Language:English
Subjects:
Algebra
Algorithms
Attribution Theory
Causal Models
Comparative Analysis
Correlation
Customization
Data
Data Collection
Data mining
Distance Education
Educational Technology
Electronic Learning
Equations (Mathematics)
Evaluation Methods
Experiments
Filtering
Geometric Concepts
Mathematical analysis
Matrices
Measurement
Personalized
Prediction
Predictor Variables
Ratings
Recommendations
Recommender systems
Research Methodology
Similarity
Statistical Analysis
Surveys
Validity
Citations: Items that cite this one
Online Access:Get full text
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Description
Summary:There are usually limited user evaluation of resources on a recommender system, which caused an extremely sparse user rating matrix, and this greatly reduce the accuracy of personalized recommendation, especially for new users or new items. This paper presents a recommendation method based on rating prediction using causal association rules. First, users and items are mapped into two feature vectors, which would be minded later to get the causal association rules from the perspective of data mining; then based on the casual association rules, the authors create a preference matrix which would predict the rating of the items that users have not rated; finally a nearest neighbor similarity measure method is designed for personalized recommendation. Experiment shows that the algorithm efficiently improves the recommendation comparing to traditional methods.
ISSN:1539-3100
1539-3119
DOI:10.4018/IJDET.2016070102