.png){kind=icon}
Return
Recommendation algorithm of probabilistic matrix factorization based on directed trust
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compeleceng.2021.107206.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper focuses on improving the performance of recommender systems by the use of social trust information. Probabilistic matrix factorization is a classic algorithm for recommender systems. However, both the rating matrix and trust matrix become sparser, which makes the recommended results inaccurate. We propose a hybrid method based on probabilistic matrix factorization and directed trust. First, we apply the probabilistic matrix factorization approach to break down the trust matrix. Thus, the potential preferences of users, considering trusters and trustees, are obtained. This approach alleviates the problem of the sparsity of the trust matrix. Second, to capture the trust relations among users, we modify undirected trust to directed trust, since a user has different preferences when he is treated as a truster or trustee. Last, the two algorithms are combined to predict ratings. Experiments involving two datasets show that the proposed algorithm is superior to existing benchmark algorithms.
Keywords:
Recommender system
Social recommendation
Probabilistic matrix factorization
Directed trust
Implicit feedback
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
C
IF:
4.9
Papers:
6.7K
Citations:
1.3W
Organization
Cited Papers
Unifying user similarity and social trust to generate powerful recommendations for smart cities using collaborating filtering-based recommender systems.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
ShareSave
ShareSaveShareSaveShareSave
ShareSave
View more