# Issues and Solutions in Deep Learning-Enabled Recommendation Systems within the E-Commerce Field
## 3 Literature Studies
### Autoencoders Studies  
Another study used a knowledge graph to solve the cold-start problem and improve the accuracy of the recommendation system, as using the Autoencoder Neural Network in the hidden layer also processed the sparsity problem based on the word2 vec approach. The edges that are between the KG and Autoencoder nodes of the system are labeled by using a Semantics-Aware Autoencoder (SemAuto). The method's details are investigated, and it is demonstrated how the SemAuto model can be used to produce content-based descriptions for suggested goods. \"Both the point and binary description styles were tried, leveraging different sorts of information available in DBpedia, to see how the efficiency of the proposed description varied depending on the selected information used to feed SemAuto (categorical and factual)\" [59]. The proposed method is compared with two kinds of Matrix Factorization (MF) algorithms, Weighted Regularized Matrix Factorization (WRMF) and Bayesian Personalized Matrix Factorization (BPMF), and another state-of-the-art method, which is a single-layer autoencoder. The Precision, Recall, F-1 score, NDCG, and aggregate diversity measurements are used to measure the performance of this work. Experiments were undertaken on three datasets (MovieLens 20M, Amazon Digital Music, and LibraryThing). In terms of accuracy, SemAuto was found to outperform baselines based on all evaluation metrics only with the Amazon Digital Music data set for different numbers of neighbors by k(40,50) [59].  
A denoising autoencoder (DAE) is a type of autoencoder used in another study. In this study, user rating trend information was employed to predict the rating for recommending the first N items using rating-trend collaborative denoising autoencoder (UT-CDAE) technology. Coding the users' rating trends is a crucial concern when dealing with faulty records. A user with higher rating behavior must be learned to learn the hidden features of other users. The rating trend of users is incorporated in this proposed model by employing two additional node vectors, High Tendency User Node (HTUN) and Low Tendency User Node (LTUN), in a simple DAE architecture. Low-value ratings are encoded by one of the node vectors, while high-value ratings are encoded by the participation of both node vectors. The study was based on measurement methods for scaling ratings of TOP-N recommendations were accuracy (P @ N), recall (R @ N), MAP, NDCG, NDCG @ N, and MRR. The proposed method (UT-CDAE) was compared to the performance of the basic noise removal autoencoder (DAE) and collaborative noise removal autoencoder (CDAE) for the ML-100K and ML-1M datasets in terms of several sequencing evaluation criteria. The performance of UT-CDAE is compared to three recent deep learning-based approaches (Caser, Neural Collaborative Filtering (NCF), and Enhanced Collaborative Auto-encoder (ECAE)) to demonstrate its utility for the ML-1M dataset. Aside from competing methodologies such as DAE and CDAE, in terms of several assessment criteria, the UT-CDAE's performance and robustness in proposing top-N recommendations to users have increased [60].