Name
Title | ||
|---|---|---|
SPMF: A Social Trust and Preference Segmentation-based Matrix Factorization Recommendation Algorithm. |
Abstract | ||
|---|---|---|
The traditional social recommendation algorithm ignores the following fact: the preferences of users with trust relationships are not necessarily similar, and the consideration of user preference similarity should be limited to specific areas. A social trust and preference segmentation-based matrix factorization (SPMF) recommendation system is proposed to solve the above-mentioned problems. Experimental results based on the Ciao and Epinions datasets show that the accuracy of the SPMF algorithm is significantly higher than that of some state-of-the-art recommendation algorithms. The proposed SPMF algorithm is a more accurate and effective recommendation algorithm based on distinguishing the difference of trust relations and preference domain, which can support commercial activities such as product marketing. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Information Retrieval | Journal |
Volume | Citations | PageRank |
abs/1903.04489 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Peng | 1 | 138 | 24.48 |
| Baogui Xin | 2 | 10 | 3.72 |