# Issues and Solutions in Deep Learning-Enabled Recommendation Systems within the E-Commerce Field
## 4 Findings and Discussion
### Issues of Recommender Systems  
The main challenges for recommender systems, whether they are based on deep learning models or machine learning, are:Accuracy  
Providing accurate recommendations is one of the main issues recommendation systems suffer from. The quality of data and the approach used will determine the accuracy of the particular system. Scalability  
The rapid growth of data in e-commerce applications makes it difficult for traditional recommendation systems to generate recommendations, as the traditional ML algorithms had a linear relation to a number of products and users. For example, Amazon recommends to more than 20 million clients an existing 18 million products. Synonymy  
Sometimes the same product is called by different names which will generate some conflict in many recommendation systems, such as collaborative filtering-based approaches. For example, a \"TV\" can be called \"Television\" and recommendation systems treat them as a different product. Shilling assaults  
This challenge represents malicious users that provide incorrect harmful information about products such as fake and negative or positive reviews. This attack mostly affects collaborative filtering techniques, but not product-based collaborative filtering approaches. Privacy  
Data privacy is a major issue in any recommendation system, as most users are concerned when sharing their data for security and privacy reasons. However, the more data is fed into the recommendation system, the better the recommendation becomes. Collaborative filtering techniques are more popular because of privacy issues, as they tend to store the users' sensitive data, such as ratings and reviews, in central repositories. Therefore, any leak with these repositories results in privacy issues. Latency problem  
Newly added products to an e-commerce application suffers from delays in recommending these products to users, as no rating is given to them. Therefore, there is a long delay in recommending a newly added product, which is a frequent event. Context awareness  
Context-based recommenders aggregate all details such as location, language, preferences, etc., thus, the performance of recommendations can be affected greatly depending on the data collected for each user to construct his/her context circumstances. Cold-start problem  
This issue affects recommending new items in the magazine or annotating new user profiles. The recommender usually depends on existing data such as product ratings or user preferences. Newly introduced data are fresh and recommended based on that, which results in inaccurate results. Possible Solutions to Recommender Systems Issues  
In order to deal with the continuous challenges faced by the relevant approaches in the recommendation systems, there are some solutions:  
Improving accuracy:  
Different scholars have tested different models to improve the overall accuracy. [PERSON] et al. show that using RBM models in conjunction with Singular Value Decomposition (SVD) results in predictions that are more accurate than Netflix's suggested viewing a list of recommendation systems, such as sparsity and scalability. Similarly, Collaborative Denoising Autoencoder (CDAE), which [PERSON] et al. (2016) suggest for a top-N recommendation, is created by recreating the dense form of user preferences. They show how CDAE's three primary elements--the translating function, transfer functions, and corruption level--affect the accuracy performance. * Scalability: Boltzmann machines are used by [PERSON] et al. (2009) to extract latent factors from users and items in order to generate predictions over huge datasets. In order to increase the scalability of RBM-based CF, [PERSON] (2010) suggests a number of solutions including distributed computing, method ensembles, and distributed processing with shared memory. According to the author, parallel computing is more effective than other methods in terms of the level of recommendations [67]. * Cold-start problems and sparsity: [PERSON] et al.