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A Recommendation Approach for Rating Prediction Based on User Interest and Trust Value
Hailong Chen1, Haijiao Sun1, Miao Cheng1
1Department of Computer Science and Technology, Harbin University of Science and Technology, Harbin, Heilongjiang 150000, China.
Computational Intelligence and Neuroscience
|March 22, 2021
Summary
This study introduces an improved collaborative filtering algorithm to address data sparsity in recommendation systems. The enhanced method boosts recommendation accuracy by integrating Bhattacharyya similarity and user trust mechanisms.
Area of Science:
- Computer Science
- Artificial Intelligence
- Information Retrieval
Background:
- Collaborative filtering is a key algorithm in personalized recommendation systems.
- Traditional collaborative filtering suffers from data sparsity, leading to inaccurate recommendations and low efficiency.
Purpose of the Study:
- To propose an improved collaborative filtering algorithm that overcomes data sparsity issues.
- To enhance recommendation accuracy and efficiency in personalized systems.
Main Methods:
- Introduced Bhattacharyya similarity to address reliance on common scoring items.
- Incorporated trust weights for direct and indirect user trust calculation via a trust transfer mechanism.
- Integrated user similarity and trust for prediction using a trust weighting method.
Main Results:
- The proposed algorithm demonstrates improved prediction accuracy compared to traditional methods.
- Experimental results validate the effectiveness of the enhanced collaborative filtering approach.
Conclusions:
- The improved collaborative filtering algorithm effectively mitigates data sparsity problems.
- The integration of similarity and trust mechanisms enhances the performance of recommendation systems.
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