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

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.

Beyond the Trends: Evolution and Future Directions in Music Recommender Systems Research

“…By integrating content-based filtering, collaborative filtering, and knowledge graphs, this hybrid recommendation system advances media recommendations grounded on user interactions and preferences [38,40]. Unlike conventional recommendation algorithms, this approach leverages music genes, user and item label data, and deep learning techniques to surmount their limitations [41]. Specifically, the hybrid model, RCTR-SMF, synergizes social matrix factorization and collaborative topic regression to tackle sparsity issues and enhance prediction accuracy in music artist recommendations [41].…”

“…Unlike conventional recommendation algorithms, this approach leverages music genes, user and item label data, and deep learning techniques to surmount their limitations [41]. Specifically, the hybrid model, RCTR-SMF, synergizes social matrix factorization and collaborative topic regression to tackle sparsity issues and enhance prediction accuracy in music artist recommendations [41].…”

Matrix Factorization Collaborative-Based Recommender System for Riyadh Restaurants: Leveraging Machine Learning to Enhance Consumer Choice

“…On the other hand, CF is a technique frequently employed in recommender systems that predicts a user's preferences based on the preferences of other users who have similar tastes [13]. One of the most effective recommendation techniques among CF-based methods is matrix factorization (MF) [10,14,15].…”

“…Building upon the proven effectiveness of matrix factorization in providing personalized recommendations to users [7][8][9][10], this paper incorporates matrix factorization approaches for restaurant recommendations in Riyadh. Specifically, we utilize three matrix factorization techniques, namely, non-negative matrix factorization (NMF), singular value decomposition (SVD), and optimized singular value decomposition (SVD++), to implement a collaborative filtering recommender system.…”

The Influence of Social Stratification on Trust in Recommender Systems

“…Social matrix factorization (SMF) is another approach that integrates social information into the matrix factorization process by considering the social network connections between users and their interactions with items [36]. This approach can effectively handle the data sparsity issue and improve the accuracy of recommendations.…”