In:
Journal of Physics: Conference Series, IOP Publishing, Vol. 2010, No. 1 ( 2021-09-01), p. 012028-
Abstract:
The rapid development of the Internet has brought convenience to people and has also produced the problem of “information overload”. In view of the traditional collaborative filtering algorithm facing some bottlenecks to be solved, this study proposes a collaborative filtering algorithm that combines similarity and trust. First of all, in view of the large deviation of traditional similarity calculation and prediction of user ratings, this study proposes an optimized Pearson correlation coefficient calculation method; secondly, the trust relationship is established based on the user’s rating of the common project, and the trust relationship between users who do not have a direct trust relationship is established through the transfer of trust; then find the nearest neighbor set of the target user through the fusion of user similarity and trust; finally, the item is scored and predicted to generate a recommendation list. Experimental results show that the algorithm proposed in this study can effectively improve the accuracy of recommendation.
Type of Medium:
Online Resource
ISSN:
1742-6588
,
1742-6596
DOI:
10.1088/1742-6596/2010/1/012028
Language:
Unknown
Publisher:
IOP Publishing
Publication Date:
2021
detail.hit.zdb_id:
2166409-2
