Menu
×
Folly Beach Library
9 a.m. - 5:30 p.m.
Phone: (843) 588-2001
Edgar Allan Poe/Sullivan's Island Library
Closed for renovations
Phone: (843) 883-3914
West Ashley Library
9 a.m. – 7 p.m.
Phone: (843) 766-6635
Wando Mount Pleasant Library
9 a.m. – 8 p.m.
Phone: (843) 805-6888
Village Library
9 a.m. - 6 p.m.
Phone: (843) 884-9741
St. Paul's/Hollywood Library
9 a.m. – 8 p.m.
Phone: (843) 889-3300
Otranto Road Library
9 a.m. – 8 p.m.
Phone: (843) 572-4094
Mt. Pleasant Library
9 a.m. – 8 p.m.
Phone: (843) 849-6161
McClellanville Library
9 a.m. - 6 p.m.
Phone: (843) 887-3699
Keith Summey North Charleston Library
9 a.m. – 8 p.m.
Phone: (843) 744-2489
John's Island Library
9 a.m. – 8 p.m.
Phone: (843) 559-1945
Hurd/St. Andrews Library
9 a.m. – 8 p.m.
Phone: (843) 766-2546
Miss Jane's Building (Edisto Library Temporary Location)
9 a.m. - 4 p.m.
Phone: (843) 869-2355
Dorchester Road Library
9 a.m. – 8 p.m.
Phone: (843) 552-6466
John L. Dart Library
9 a.m. – 7 p.m.
Phone: (843) 722-7550
Baxter-Patrick James Island
9 a.m. – 8 p.m.
Phone: (843) 795-6679
Main Library
9 a.m. – 8 p.m.
Phone: (843) 805-6930
Bees Ferry West Ashley Library
9 a.m. – 8 p.m.
Phone: (843) 805-6892
Mobile Library
9 a.m. - 5 p.m.
Phone: (843) 805-6909
Today's Hours
Folly Beach Library
9 a.m. - 5:30 p.m.
Phone: (843) 588-2001
Edgar Allan Poe/Sullivan's Island Library
Closed for renovations
Phone: (843) 883-3914
West Ashley Library
9 a.m. – 7 p.m.
Phone: (843) 766-6635
Wando Mount Pleasant Library
9 a.m. – 8 p.m.
Phone: (843) 805-6888
Village Library
9 a.m. - 6 p.m.
Phone: (843) 884-9741
St. Paul's/Hollywood Library
9 a.m. – 8 p.m.
Phone: (843) 889-3300
Otranto Road Library
9 a.m. – 8 p.m.
Phone: (843) 572-4094
Mt. Pleasant Library
9 a.m. – 8 p.m.
Phone: (843) 849-6161
McClellanville Library
9 a.m. - 6 p.m.
Phone: (843) 887-3699
Keith Summey North Charleston Library
9 a.m. – 8 p.m.
Phone: (843) 744-2489
John's Island Library
9 a.m. – 8 p.m.
Phone: (843) 559-1945
Hurd/St. Andrews Library
9 a.m. – 8 p.m.
Phone: (843) 766-2546
Miss Jane's Building (Edisto Library Temporary Location)
9 a.m. - 4 p.m.
Phone: (843) 869-2355
Dorchester Road Library
9 a.m. – 8 p.m.
Phone: (843) 552-6466
John L. Dart Library
9 a.m. – 7 p.m.
Phone: (843) 722-7550
Baxter-Patrick James Island
9 a.m. – 8 p.m.
Phone: (843) 795-6679
Main Library
9 a.m. – 8 p.m.
Phone: (843) 805-6930
Bees Ferry West Ashley Library
9 a.m. – 8 p.m.
Phone: (843) 805-6892
Mobile Library
9 a.m. - 5 p.m.
Phone: (843) 805-6909
Patron Login
menu
Item request has been placed!
×
Item request cannot be made.
×
![loading](/sites/all/modules/hf_eds/images/loading.gif)
Hybrid Recommendation Network Model with a Synthesis of Social Matrix Factorization and Link Probability Functions.
Item request has been placed!
×
Item request cannot be made.
×
![loading](/sites/all/modules/hf_eds/images/loading.gif)
- Author(s): Kumar, Balraj; Sharma, Neeraj; Sharma, Bhisham; Herencsar, Norbert; Srivastava, Gautam
- Source:
Sensors (14248220); Mar2023, Vol. 23 Issue 5, p2495, 20p- Subject Terms:
- Source:
- Additional Information
- Abstract: Recommender systems are becoming an integral part of routine life, as they are extensively used in daily decision-making processes such as online shopping for products or services, job references, matchmaking for marriage purposes, and many others. However, these recommender systems are lacking in producing quality recommendations owing to sparsity issues. Keeping this in mind, the present study introduces a hybrid recommendation model for recommending music artists to users which is hierarchical Bayesian in nature, known as Relational Collaborative Topic Regression with Social Matrix Factorization (RCTR–SMF). This model makes use of a lot of auxiliary domain knowledge and provides seamless integration of Social Matrix Factorization and Link Probability Functions into Collaborative Topic Regression-based recommender systems to attain better prediction accuracy. Here, the main emphasis is on examining the effectiveness of unified information related to social networking and an item-relational network structure in addition to item content and user-item interactions to make predictions for user ratings. RCTR–SMF addresses the sparsity problem by utilizing additional domain knowledge, and it can address the cold-start problem in the case that there is hardly any rating information available. Furthermore, this article exhibits the proposed model performance on a large real-world social media dataset. The proposed model provides a recall of 57% and demonstrates its superiority over other state-of-the-art recommendation algorithms. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Sensors (14248220) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
Contact CCPL
Copyright 2022 Charleston County Public Library Powered By EBSCO Stacks 3.3.0 [350.3] | Staff Login
No Comments.