Collaborative Filtering Algorithm Based on Trust and Information Entropy
| Title | Collaborative Filtering Algorithm Based on Trust and Information Entropy |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Kang, Anqi |
| Conference Name | 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) |
| Date Published | Oct. 2018 |
| Publisher | IEEE |
| ISBN Number | 978-1-5386-7516-8 |
| Keywords | Collaboration, collaborative filtering, collaborative filtering algorithm, computer theory, Entropy, Filtering, filtering algorithms, Human Behavior, human computer interaction, human factors, human trust, information entropy, information entropy theory, Pearson similarity, Prediction algorithms, pubcrawl, recommendation system, recommender systems, resilience, Resiliency, robots, Scalability, security, time decay, time decay function, Trust, trust relationship, trust similarity, weighted information entropy |
| Abstract | In order to improve the accuracy of similarity, an improved collaborative filtering algorithm based on trust and information entropy is proposed in this paper. Firstly, the direct trust between the users is determined by the user's rating to explore the potential trust relationship of the users. The time decay function is introduced to realize the dynamic portrayal of the user's interest decays over time. Secondly, the direct trust and the indirect trust are combined to obtain the overall trust which is weighted with the Pearson similarity to obtain the trust similarity. Then, the information entropy theory is introduced to calculate the similarity based on weighted information entropy. At last, the trust similarity and the similarity based on weighted information entropy are weighted to obtain the similarity combing trust and information entropy which is used to predicted the rating of the target user and create the recommendation. The simulation shows that the improved algorithm has a higher accuracy of recommendation and can provide more accurate and reliable recommendation service. |
| URL | https://ieeexplore.ieee.org/document/8549962/ |
| DOI | 10.1109/ICIIBMS.2018.8549962 |
| Citation Key | kang_collaborative_2018 |
- Prediction algorithms
- weighted information entropy
- trust similarity
- trust relationship
- trust
- time decay function
- time decay
- security
- Scalability
- robots
- Resiliency
- resilience
- recommender systems
- recommendation system
- pubcrawl
- collaboration
- Pearson similarity
- information entropy theory
- information entropy
- human trust
- Human Factors
- human computer interaction
- Human behavior
- filtering algorithms
- Filtering
- Entropy
- computer theory
- collaborative filtering algorithm
- collaborative filtering