Publication detail

Hybrid Recommendation Network Model with a Synthesis of Social Matrix Factorization and Link Probability Functions

KUMAR, B. SHARMA, N. SHARMA, B. HERENCSÁR, N. SRIVASTAVA, G.

Original Title

Hybrid Recommendation Network Model with a Synthesis of Social Matrix Factorization and Link Probability Functions

Type

journal article in Web of Science

Language

English

Original 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.

Keywords

collaborative filtering; topic modelling; recommendation system; collaborative topic regression; social matrix factorization; social network; item network structure

Authors

KUMAR, B.; SHARMA, N.; SHARMA, B.; HERENCSÁR, N.; SRIVASTAVA, G.

Released

23. 2. 2023

Publisher

MDPI

Location

Basel

ISBN

1424-8220

Periodical

SENSORS

Year of study

23

Number

5

State

Swiss Confederation

Pages from

1

Pages to

20

Pages count

20

URL

https://www.mdpi.com/1424-8220/23/5/2495

Full text in the Digital Library

http://hdl.handle.net/11012/209275

BibTex

@article{BUT182960,
  author="Balraj {Kumar} and Neeraj {Sharma} and Bhisham {Sharma} and Norbert {Herencsár} and Gautam {Srivastava}",
  title="Hybrid Recommendation Network Model with a Synthesis of Social Matrix Factorization and Link Probability Functions",
  journal="SENSORS",
  year="2023",
  volume="23",
  number="5",
  pages="20",
  doi="10.3390/s23052495",
  issn="1424-8220",
  url="https://www.mdpi.com/1424-8220/23/5/2495"
}