Paper Info
Title
Two-Sided Regularization Model Based On Probabilistic Matrix Factorization And Quantum Similarity For Recommender Systems
Abstract
Nowadays, with the advent of the age of Web 2.0, several social recommendation methods that use social network information have been proposed and achieved distinct developments. However, the most critical challenges for the existing majority of these methods are: (1) They tend to utilize only the available social relation between users and deal just with the cold-start user issue. (2) Besides, these methods are suffering from the lack of exploitation of content information such as social tagging, which can provide various sources to extract the item information to overcome the cold-start item and improve the recommendation quality. In this paper, we investigated the efficiency of data fusion by integrating multi-source of information. First, two essential factors, user-side information, and item-side information, are identified. Second, we developed a novel social recommendation model called Two-Sided Regularization (TSR), which is based on the probabilistic matrix factorization method. Finally, the effective quantum-based similarity method is adapted to measure the similarity between users and between items into the proposed model. Experimental results on the real dataset show that our proposed model TSR addresses both of cold-start user and item issues and outperforms state-of-the-art recommendation methods. These results indicate the importance of incorporating various sources of information in the recommendation process.
Year
DOI
Venue
2020
10.1142/S1793962320500567
INTERNATIONAL JOURNAL OF MODELING SIMULATION AND SCIENTIFIC COMPUTING
Keywords
DocType
Volume
Social recommendation, explicit friendship, implicit friendship, correlated items, quantum mechanics
Journal
11
Issue
ISSN
Citations 
6
1793-9623
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Waleed Reafee100.34
Marwa Alhazmi200.34
Naomie Salim342448.23