| Title | A Hybrid Recommendation Algorithm Based on Heuristic Similarity and Trust Measure |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Yang, Chao, Chen, Xinghe, Song, Tingting, Jiang, Bin, Liu, Qin |
| Conference Name | 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) |
| Date Published | aug |
| Keywords | clustering, clustering algorithm, Clustering algorithms, cold start, Collaboration, collaborative filtering, composability, compositionality, Computing Theory and Trust, Conferences, data sparsity, Heuristic algorithms, heuristic similarity, hybrid collaborative filtering recommendation algorithm, K nearest neighbor recommendation, Matrix decomposition, multiple similarity influence factors, nearest neighbour methods, pattern clustering, Prediction algorithms, pubcrawl, recommedation algorithm, recommendation list, recommender systems, similarity measure, SIMT collaborative filtering algorithm, trust measure, trust model, trust propagation theory, Trusted Computing, user trust network, user trust relationship computing model, Weight measurement |
| Abstract | 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. |
| DOI | 10.1109/TrustCom/BigDataSE.2018.00196 |
| Citation Key | yang_hybrid_2018 |
A Hybrid Recommendation Algorithm Based on Heuristic Similarity and Trust Measure