Today’s arena of global village organizations, social applications, and commercial websites provides huge information about products, individuals, and activities. This is leading to a plethora of content that requires effective handling to obtain the desired information. A recommendation system (RS) suggests relevant items to the user according to his/her desired preference. It processes various information related to users and items. However, RSs suffer from data sparsity. Generally, deep learning techniques are used in RSs for deep analysis of item contents to create precise recommendations. However, the effective handling of user reviews in parallel with item reviews is still an open research domain that can be further explored. In this paper, a hybrid model that handles both user and item metadata concurrently is proposed with the aim of solving the sparsity problem. To demonstrate the viability of the proposed methodology, a series of experiments was performed on three real-world datasets. The results show that the proposed model outperforms other state-of-the-art approaches to the best of our knowledge.

当今地球村组织、社交应用程序和商业网站的舞台提供了有关产品、个人和活动的大量信息。这导致需要有效处理才能获得所需信息的大量内容。推荐系统 (RS) 根据用户所需的偏好向用户推荐相关项目。它处理与用户和项目相关的各种信息。然而，RS 受到数据稀疏性的影响。通常，深度学习技术在 RS 中用于对项目内容进行深度分析以创建精确推荐。然而，在项目评论的同时有效处理用户评论仍然是一个开放的研究领域，可以进一步探索。在本文中，为了解决稀疏性问题，提出了一种同时处理用户和项目元数据的混合模型。为了证明所提出方法的可行性，在三个真实世界的数据集上进行了一系列实验。结果表明，就我们所知，所提出的模型优于其他最先进的方法。