# State-of-the-Art Survey on Deep Learning-Based Recommender Systems for E-Learning
## 3 Classification Method
### Classification Based on Recommendation Techniques
#### 3.1.6 Multi-Criteria RS  
Both traditional and context-aware RSs are implemented based on a single rating value \(r\) to represent the degree to which users are interested in items. Although single-rating RSs have been implemented in applications across several domains, research has shown that their functionality is limited because the likeness of an item by users may depend on several items' attributes [27]. Interestingly, RS researchers in industries and academic institutions have already started shifting toward multi-criteria rating systems [28]. As an illustration, Zagat's guide ([[https://www.zagat.com](https://www.zagat.com)]([https://www.zagat.com](https://www.zagat.com)) accessed on 5 July 2022), a restaurant guide that issues a restaurant rating criterion based on three categories (food, service, decor), online shops such as Rakuten ([[http://www.rakuten.com/](http://www.rakuten.com/)]([http://www.rakuten.com/](http://www.rakuten.com/)) accessed on 5 July 2022), and retailers of consumer electronics ([[https://circuitcity.com](https://circuitcity.com)]([https://circuitcity.com](https://circuitcity.com)) accessed on 5 July 2022) are examples of systems that are developed based on multi-criteria RSs. We are familiar with the form and how ratings take place in traditional recommendation approaches from the beginning of this section. We know how the rating functions work, how to compute similarities between users (Equations (6) and (9)) if necessary, as well how to use the similarity \(sim(u,v)\) to calculate ratings (Equations (2) and (3)). Similarly, multi-criteria recommenders are designed to achieve the same goal of finding products/resources that might be useful and interesting to users. They vary only by acquiring more information about items from the users that is useful in making meaningful recommendations. In multi-criteria recommenders, the rating function \(f(u,i)\) is of the form \(f\,:u\times i\to r_{0},\,r_{1},r_{2},\ldots,r_{n}\), where \(r_{0}\) is the overall rating, similar to \(r\) in traditional and context-aware RSs, and \(r_{k}\) are the criteria ratings. Table 1 is a sample of ratings in multi-criteria systems using four criteria and an overall rating to predict user preferences on items [27]. The ratings with question marks (\(7\),\(7\),\(7\),\(7\)) are the unknown ratings that need to be predicted. [PERSON], in [28], outlined two methods for calculating similarities between users or items for rating predictions in multi-criteria RSs. One of the proposed methods uses the same similarity metrics discussed in Equations (6) and (9) to make predictions by making some minor adjustments to account for all \(n+1\) different similarities, which are briefly demonstrated below:  
We know that for each \(u\) and \(i\), the utility function \(f(u,i)\) produces \(n+1\) different ratings, where \(n\) is the number of criteria used. \(f(u,i)=r_{0},\,r_{1},r_{2},\ldots,r_{n}\), which requires calculating \(n+1\) similarities, one similarity for \(r_{0}\) and others for each rating \(r_{k}\). Let \(sim_{m}(u,v)\) \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**User/Item** & **Item1** & **Item2** & **Item3** & **Item4** \\ \hline User1 & \(2.5_{4,3,2,1}\) & \(3.5_{5,5,2,4,3}\) & \(4_{4,4,5,5,2}\) & \(1_{1,1,0,2,1}\) \\ User2 & \(7_{2,3,7,2}\) & \(1_{0,0,0,4}\) & \(2_{3,2,2,1}\) & \(0_{0,0,0,0}\) \\ User3 & \(7_{2,3,2,2}\) & \(4_{4,4,5,5,2}\) & \(5_{5,5,5,5}\) & \(7_{2,3,2,7}\) \\ User4 & \(4_{5,4,5,5,4}\) & \(1.5_{2,1,1,2}\) & \(5_{5,5,5,5,5}\) & \(0_{0,0,0,0}\) \\ User5 & \(3_{4,3,2,3}\) & \(7_{2,3,7,2,7}\) & \(7_{2,3,7,2,7}\) & \(2_{5,3,3,2,2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Example of Four Criteria Rating Problem.