# A Comprehensive Survey of Recommender Systems Based on Deep Learning
## 3 Overview of the Recommender Systems  
Early recommender systems were primarily based on collaborative filtering and content-based recommendation. User-based collaborative filtering methods were introduced in 1992, while item-based collaborative filtering methods emerged in 1999. Content-based recommendation methods also began to appear during this period, utilizing item attributes and user preferences for recommendations. In the 21 st century, with the rise of social networks and the development of deep learning technology, social recommendation methods gradually gained attention. Similarly, cross-domain recommendation methods also started to emerge, allowing the application of a user's historical behavior in one domain to recommendations in other domains. In recent years, with the proliferation of mobile devices and online shopping, sequence recommendation methods have become increasingly important. These methods consider user behavior sequences, such as purchase history or browsing history, to provide more personalized recommendations. Deep learning methods can handle large-scale data and multi-modal information, enhancing the effectiveness of recommendations. Reinforcement learning methods have also been applied to recommender systems, optimizing recommendation strategies via user interactions. The development history of recommender systems can be divided into several key stages, each producing different types of recommendation methods. A summary of some important recommender system methods is provided in the following Figure 1.  
New technologies, compared to old ones, are not a complete revolution. New technologies are built on the foundation laid by their predecessors, incorporating improvements rather than being entirely different. The field of recommender systems is continuously evolving, with new methods and technologies emerging to adapt to evolving user demands and data environments. Currently, efforts are underway to refine the integration of deep learning for improved recommendation results. Therefore, the journey of recommender systems is a dynamic and evolving process. We will begin by introducing the most fundamental approaches, combining deep learning with collaborative filtering and content-based recommendation. Collaborative filtering is one of the first and fundamental recommender systems. In 1992, [PERSON] et al. [47] introduced the concept of collaborative filtering. The key idea lies in finding the nearest neighbors using the interaction matrix of users and items, and then generating various forms of recommendations based on the information from these neighbors, which is essentially a process of matrix completion. The core concept of collaborative filtering algorithms is to provide product recommendations or predictions based on similar users or similar items, encompassing two dimensions: neighborhood-based and model-based approaches. The key to collaborative filtering recommendations is the computation of similarity between different users and different items. Over the past three decades, collaborative filtering has been widely used by people. However, as time has progressed, its scalability limitations have become apparent, making it less suitable for scenarios with large datasets. People have turned to machine learning and data mining algorithms to learn and recognize complex patterns using training data. Currently, one of the most common paradigms for building deep learning recommendation algorithms is the use of multi-layer perceptions, which can introduce nonlinear transformations into existing collaborative filtering methods. Since then, the research of recommender systems has shifted towards deep learning-based recommendation [48]. To address the complexities of user behavior, neural network-based models have become increasingly favored by researchers [49].