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Multi-criteria recommender system is used to make user experience easier or make things easy for each and every user. If we take a look on business perspective, then this system is used to grow a business.

Table 3.5 Comparison among existing methods in MCRS.

Author name	Research work	Work done	Method name	Challenges
Yong Zheng	Utility-Based Multi-Criteria Recommender System (2019)	Implemented a new recommender system approach which dominates many the traditional approaches.	Utility-based approach.	Issue of overexpectation, which may contribute finer-grained recommendation models
Dharahas Tallapally, Rama Syamala Sreepada, Bidyut kr. Patra, Korra Sathya Babu	User Preference Learning in Multi-Criteria Recommendation Using Stacked Auto Encoders	Implemented a recommender system using extended version of autoencoder named as stacked autoencoder.	Stacked Autoencoder, An unsupervised deep neural network approach.	This approach is still now in improvement state, and this approach cannot work in user review system.
Mohammed Wasid, Rashid Ali	An Improved recommender System based on Multi-criteria Clustering Approach (2018)	Implemented Multicriteria recommender system using Clustering approach which gives getting better result than traditional recommender Systems.	K-means Clustering, Mahalanobis Distance.	This particular algorithm assumes that each client has individual opinion which is dissimilar to each other for every criterion.
Yong Zheng	Situation-Aware Multi-Criteria Recommender System: Using Criteria Preferences as Contexts (2017)	Implemented a recommender system using criteria preference as context approach that gives a better result.	Aggregation approach, Full Contextual Model, Partial Contextual Model, Hybrid Contextual model.	Does not work well for all the cases. Its need improvement.
CataldoMusto, Marco de Gemmis, Giovanni Semeraro, Pasquale Lops	A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users’ Reviews (2017)	Implemented a recommender system using users review.	Taking out Aspects and Sentiment from Reviews, then feed it in multi-criteria recommender algorithm based on collaborative filtering technique.	This research work is still in improvement state. It can be improved further.

In business point of view, if a customer comes in any platform, then the main work of the owner and the staff is to satisfy the customer needs. They will make sure that the customer must have a good experience and got their desired thing. This is the main work of a recommender system. For this particular reason, many companies are adopting this multi-criteria recommender system [33].

Here are some benefits in businesses perspective that can be achieved by using MCRS: