A Hybrid Recommendation Algorithm Based on Heuristic Similarity and Trust Measure
| Autor: | Bin Jiang, Ting-Ting Song, Xinghe Chen, Qin Liu, Chao Yang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Measure (data warehouse)
Heuristic (computer science) Computer science 020206 networking & telecommunications 02 engineering and technology Similarity measure k-nearest neighbors algorithm Cold start Similarity (network science) 0202 electrical engineering electronic engineering information engineering Collaborative filtering 020201 artificial intelligence & image processing Cluster analysis Algorithm |
| Zdroj: | TrustCom/BigDataSE |
| Popis: | In this paper, we propose a hybrid collaborative filtering recommendation algorithm based on heuristic similarity and trust measure, in order to alleviate the problem of data sparsity, cold start and trust measure. Firstly, a new similarity measure is implemented by weighted fusion of multiple similarity influence factors obtained from the rating matrix, so that the similarity measure becomes more accurate. Then, a user trust relationship computing model is implemented by constructing the user's trust network based on the trust propagation theory. On this basis, a SIMT collaborative filtering algorithm is designed which integrates trust and similarity instead of the similarity in traditional collaborative filtering algorithm. Further, an improved K nearest neighbor recommendation based on clustering algorithm is implemented for generation of a better recommendation list. Finally, a comparative experiment on FilmTrust dataset shows that the proposed algorithm has improved the quality and accuracy of recommendation, thus overcome the problem of data sparsity, cold start and trust measure to a certain extent. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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