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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU147779" target="_blank" >RIV/00216305:26220/23:PU147779 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/1424-8220/23/5/2495" target="_blank" >https://www.mdpi.com/1424-8220/23/5/2495</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s23052495" target="_blank" >10.3390/s23052495</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20201 - Electrical and electronic engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

SENSORS

ISSN

1424-8220

e-ISSN

1424-3210

Volume of the periodical

23

Issue of the periodical within the volume

5

Country of publishing house

CH - SWITZERLAND

Number of pages

20

Pages from-to

1-20

UT code for WoS article

000947418800001

EID of the result in the Scopus database

2-s2.0-85149669006