Advanced Analytics and Deep Learning Models. Группа авторов

Чтение книги онлайн.

СКАЧАТЬ 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:

      3.5.1 Revenue

      MCRS has a big role in generating the revenue. Years of research, execution, and experiments, many researchers from all over the world made various types of recommender systems. Many different types of algorithms are executed and explored to get good accuracy. When a customer came to buy a particular thing then the MCRS shows him some similar things and accessories so that the customer will buy that too. This is one of the job of MCRS and also one of the effective ways to generate better revenue.

      3.5.2 Customer Satisfaction

      By a survey, it is seen that, many times, customers tend to look at their product recommendation from their last browsing for a better opportunity for good products. Suppose a customer browses as it and leave and after sometime he come again, then it would be a great help if their previous browsing sessions are available. It is also good for the recommender system because it will generate an item list based on the user’s previous data and recommend those items to the customer. So, this is how MCRS plays an important role in customer satisfaction.

      3.5.3 Personalization

      If we take a look into our daily life, then we will understand that we also take recommendations to buy a product. These recommendations are mostly given by our family members and friends. We followed their recommendations because we trust them. This is the sole element which is implementing the MCRS to build among system and the user. The more suitable product system will predict for the user, the more the user will trust the MCRS and will buy the recommended product. That is how by personalization a customer will come to that platform again and again.

      3.5.4 Discovery

      Every recommender system can be used as a tool. The leading companies discover new ways to apply the recommender system as a tool. For example, iTunes uses “Genius Recommendations” which is a very efficient multi-criteria recommender tool, and Amazon uses “Frequently Bought together” which can also be customized by the user also. These are some innovative ways of using the multi-criteria recommender systems.

      3.5.5 Provide Reports

      This is an integral part of personalization system. It is also used to make trust with the clients. It helps to make solid decisions about the site or the direction of a campaign. In order to create a drive-in, sales client generates offers on slow moving product based on their report.

      In the present time, online sales are generally more satisfying. So, every company wants to do something extra. An e-commerce company can use different types of filtering technique like collaborative, content-based, hybrid filtering as we have studied before to make an effective recommendation engine. For example, Amazon has super successful multi-criteria recommender engine which recommends several things as add on very efficiently and that attract a client or a customer to buy more and more things from the same platform. It follows the sole of every recommender system that the only way to truly engage with customer is to communicate with each as an individual.

3.6 Challenges of Multi-Criteria Recommender System

      There are some common challenges which every MCRS faces. Some of them are as follows.

      3.6.1 Cold Start Problem

This problem faces recommender system when a client came for the first time in that platform or when a new item is added on that platform. When someone comes for the first time in any platform, recommender system does not have any information about that client. Because the client have not rated any product or have not surfed anything. СКАЧАТЬ