# Issues and Solutions in Deep Learning-Enabled Recommendation Systems within the E-Commerce Field
## 3 Literature Studies
### Collaborative Filtering Studies  
Collaborative filtering is a strategy and matrix decomposition is a method to implement the strategy. Collaborative filtering relies primarily on personalized recommendations based on user experience and suggestions with comparable qualities or interests. Through recommendation systems, it is beneficial to gather individuals with similar interests or characteristics and provide their feedback to users in the same cluster for reference, in order to satisfy the mentality of people to refer to the opinions of others before making decisions. Collaborative filtering technology must include the following aspects: (1) a process of comparing and collecting the interest preferences of each user; (2) a lot of user information to predict personal interest preferences; (3) statistics on the degree of relevance of interest preferences to develop recommendations for users who have the same interest preferences [57].  
Recommendation services are always based on a volume of data. Depending on the nature of this data, a distinction is also made between the different types of systems. Typically, we can distinguish between content-based systems and so-called collaborative ones. There are also recommendation services adapted to different contexts and those which include in their calculation dated histories or demographic data of the users. Content-based recommendation systems suggest similar items or content that the user has previously searched for, viewed, purchased, or rated positively. For this, the system must be able to determine similarities between objects. Content analysis is therefore carried out. In the case of streaming services for music, the system, for example, evaluates the songs, taking into account their structure, to find similar ones. The collaborative technique bases the advice on the monitoring of users who share common behavior. When a group of people has previously expressed a strong interest in a certain object, the algorithm will continue to promote it [61; 62; 63; 64]. Exact information about the object is not even necessary here. Amazon uses this process extensively. Recommendation services use different learning methods. Most of the time, model or memory-based methods are used. The memory-based method uses all stored classification data and identifies similarities between users or objects. The result serves as a forecast basis for combining objects that have not yet been mined. On the other hand, model-based recommendation services work with machine learning principles. Based on the data, the system should create a mathematical model capable of forecasting user interest in a given product.  
Another approach is item-based collaborative filtering combined with an attention mechanism neural network proposed by the authors in [65]. This technique often offers high accuracy which is why it is suggested to be used in recommending items. However, the authors focused on hidden factors that might lead to better identification of item similarity, thus improving user's experience.  
The contrastive learning (CL) approach is proposed by authors of [66], that addresses the sparsity challenge in recommendation systems, as it follows the self-generated data from the customer side that the recommendation system needs. The proposed method visualizes the distribution between items and users which will result in showing graph augmentation and adding pre-configured noise to show contrastive visualization.