# Issues and Solutions in Deep Learning-Enabled Recommendation Systems within the E-Commerce Field
## 4 Findings and Discussion
### Issues of Recommender Systems
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Advantage** & **Method** & **Disadvantage** \\ \hline \multirow{4}{*}{[45]} & Achieves better performance using the MAE and & & The number of experiments in all three datasets is \\  & RMSE measures with the three datasets and & & & about 20. This is a big effort, and removing one of \\  & achieves higher prediction accuracy, and this & & the best results will reduce the efficiency of the \\  & model proves to be effective. & & results, randomizing the data \\ \hline \multirow{4}{*}{[30]} & Extracts deep features from hidden issues & & Did not perform better compared to the MF and \\  & extracted from textual user reviews to develop a & & HFT models in the movies and TV \\  & recommendation system. & & product category. \\ \hline \multirow{4}{*}{[46]} & Added information to an ads platform to improve & & It is complex and inflexible due to the \\  & recommendation performance for cold-start users & & interconnection of two models (GMF and MLP) in \\  & of an online shopping space & & the hidden layer. \\ \hline \multirow{4}{*}{[47]} & Broke the groundwork in both the rating & & \\  & estimation and the item ranking problem, and & & Extensive information on the proposal was not \\  & explanation experiments show that it can produce & & extracted, and the opaque estimation process and \\  & both compelling and readable explanations for & & expensive to train. \\ \hline \multirow{4}{*}{[48]} & Significantly improves accuracy and & & Requires a Big Data storage repository that holds \\  & interpretability in the top N recommendation task & & various formats, so it needs to be converted to \\  & compared to the most advanced methods. & & uniformity, all of which are required for \\  & Effective for extracting high-level features from & & additional work, bid, capacity, and time. In the \\  & various low-level data, especially unstructured & & optimization process, rendering with interactive \\  & data such as images, text, and audio. & & user-item pairs was rated higher by the model \\ \hline \multirow{4}{*}{[52]} & The IAAN outperforms single-component & & Does not use IIAAN category information. When \\  & models in all situations. It shows that considering & & size is greater than 100, the model does not \\  & in-carl and between-bakets at the same time & & benefit from a larger size. Overfitting can be a \\  & helps predict the next basket. & & possible cause. \\ \hline \multirow{4}{*}{[49]} & Experiments on two general datasets show that & & Constraints in application areas, each user from \\  & the proposed model is superior to traditional and & & the embedding size 100 is recommended 5 items \\  & existing deep learning-based recommendation & & that are not in the same basket, which means \\  & models, and the next basket proposal is to & & product diversification for several users, not for \\  & recommend users one basket of products & & the same basket and the same user. This can cause \\  & at a time. & & sparsity and cold-start issues. \\ \hline \multirow{4}{*}{[53]} & In performing the work with its strengths, the & & The constraints in application domains are \\  & SBPR may be aware of idiosyncratic features to & & personalized for implicit feedback in this study. \\  & detect users’ preferences. & & Not using point sort with binary sort in \\ \hline \multirow{4}{*}{[70]} & With the strengths of the study, the data & & \\  & augmentation algorithm proposed in this paper is & & \\  & effective, and the application of the inflexible is & & \\  & also possible. & & \\ \hline \multirow{4}{*}{[60]} & From the strengths of the study, a new approach is & & \\  & presented to provide accurate, diversified, and & & While removing all items from unplanned \\  & explainable recommendations. & & datasets in DBpedia leads to skipping some \\  & Also, this approach can be used to study data & & important elements of the recommendation, there \\  & quality for knowledge bases in recommendation & & is a disadvantage in these experiments that is not \\  & scenarios. Finding the best number of features to & & present in DBpedia. \\  & calculate explanations is not within the scope of & & \\  & this article & & \\ \hline \multirow{4}{*}{[62]} & Provides additional editing flexibility, which & & \\  & helps to estimate the improved top N & & \\  & recommendations. Results showed that the & & \\  & proposed model outperforms state-of-the-art & & Ignores constraints in application domains, \\  & noise-canceling autoencoder models. & & ratings below 4, which may affect the number of \\  & Provides an opportunity to learn a user’s high & (UT-CDAE) & recommendations. The second constraint of this \\  & trending percentage. & & study is that it randomly divides the data into \\  & Patterns of co-liking by users are also well learned & & training data and test the rest. \\  & during the coding process when trained & & \\  & on the user. & & \\ \hline \hline \end{tabular}
\end{table}