School of Computer Application, Lovely Professional University, Phagwara 144411, Punjab, India.
Department of Computer Science, Punjabi University, Patiala 147002, Punjab, India.
Sensors (Basel). 2023 Feb 23;23(5):2495. doi: 10.3390/s23052495.
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.
推荐系统正成为日常生活不可或缺的一部分，因为它们被广泛应用于日常决策过程中，如在线购买产品或服务、求职参考、婚姻匹配等。然而，由于稀疏性问题，这些推荐系统在提供高质量推荐方面存在不足。考虑到这一点，本研究引入了一种混合推荐模型，用于向用户推荐音乐艺术家，这是一种分层贝叶斯性质的模型，称为基于关系协同主题回归的社交矩阵分解（RCTR-SMF）。该模型利用了大量辅助领域知识，并将社交矩阵分解和链接概率函数无缝集成到基于协同主题回归的推荐系统中，以获得更高的预测准确性。在这里，主要重点是检查与社交网络和项目关系网络结构相关的统一信息以及项目内容和用户项目交互的有效性，以进行用户评分预测。RCTR-SMF 通过利用额外的领域知识来解决稀疏性问题，并且在几乎没有任何评分信息可用的情况下，可以解决冷启动问题。此外，本文还展示了该模型在大型真实社交媒体数据集上的性能。该模型的召回率为 57%，证明了其优于其他最先进的推荐算法。
