# A Comprehensive Survey of Recommender Systems Based on Deep Learning
###### Abstract  
With the increasing abundance of information resources and the development of deep learning techniques, recommender systems (RSs) based on deep learning have gradually become a research focus. Although RSs have evolved in recent years, a systematic review of existing RS approaches is still warranted. The main focus of this paper is on recommendation models that incorporate deep learning techniques. The objective is to guide novice researchers interested in this field through the investigation and application of the proposed recommendation models. Specifically, we first categorize existing RS approaches into four types: content-based recommendations, sequence recommendations, cross-domain recommendations, and social recommendation methods. We then introduce the definitions and address the challenges associated with these RS methodologies. Subsequently, we propose a comprehensive categorization framework and novel taxonomies for these methodologies, providing a thorough account of their research advancements. Finally, we discuss future developments regarding this topic. recommender systems; deep learning; social networks; sequence recommendation; cross-domain recommendation +
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to video recommendations with outstanding results [6]. This achievement served as inspiration for an increasing number of recommendation models to incorporate deep learning techniques [7]. In an RS, 'item' denotes a product or service recommended by the system to its users. Recommendation of products to a user requires the analysis of the past preferences of similar users and leveraging item information. At the same time, users' preferences are dynamically evolving [8], so researchers employ time-based/sequential methods to model users' dynamic preferences and sequential patterns that change over time [9]. The integration of recommendation techniques with neural networks has paved the way for broader application prospects [10]. Users frequently interact with multiple platforms using various services. By utilizing cross-domain recommendations, insights from different domains can be leveraged to provide more comprehensive and relevant suggestions [11]. This approach effectively tackles data sparsity, mitigates the cold start problem, and captures a more holistic view of a user's multifaceted preferences. Some studies [12; 13; 14] have indicated that social relationships between users can be effectively used to alleviate issues such as the cold start problem. People have proposed integrating information-rich social networks with neural networks and incorporating the social-relational attributes of users or items (such as friendships and tag categorizations) into traditional recommendation frameworks, thereby overcoming the limitations of conventional recommendation algorithms. However, many recommendation algorithms still fall in terms of handling data sparsity and processing large-scale data, which to some extent compromises the precision of recommendations produced [15]. RS algorithms must have the ability to deal with highly sparse data and need to scale with increasing numbers of users and items. At the same time, they must provide satisfactory results and address other issues, such as data noise and privacy protection. In the evolution of recommender systems, various challenges have arisen, such as information overload, cold start problems, long tail effects, and so on. In recent years, deep learning technology has emerged as a pivotal force in the field of recommender systems. Deep learning models, known for their exceptional performance, have become a hot spot in both academia and industry, offering solutions to many of the core challenges currently confronting recommender systems. What makes deep learning compelling is its effectiveness in addressing the numerous challenges encountered by recommender systems. First of all, deep learning models excel at learning complex user and project features, making them adept at capturing subtle differences in user behavior.