were also included in the reviews to mitigate the sparsity problem which still lies in the recommender systems [7].
Bauman et al. presented a recommendation system that suggested the items that comprised of the most significant aspects to improve the user’s overall experience. These aspects were identified using the Sentiment Utility approach [8].
Akhtar et al. presented a technique for analyzing the hotel reviews and extracted some valuable information or knowledge from them to assist the service providers as well as the customers to help them identify the loopholes and strengths in the service sector to improve their business performance [9].
Yang et al. presented a technique consisting of three main components namely aspect weight, opinion mining, and overall rating inference. The opinion mining component was responsible for extracting only the key aspects and opinions from the users’ reviews based on which it computed a rating for each extracted aspect [10].
Dong et al. presented a method for CF that merges feature similarity and feature sentiments for recommending items, that having higher priority that are similar and better than the items in the users query [11].
Wang et al. proposed an approach on solving a problem when a user is particularly new to an environment. This problem is known as cold start problem. We will discuss about the cold start problem later in this paper. Most recommender systems collect the preferences of the users on some attributes of the items [12].
Musat et al. explained a method called topic profile CF (TPCF) that solved the problems occurring due to the data sparsity problems and non-personalized ranking methods that led to difficulty in finding sufficient reliable data for making recommendations [13].
Jamroonsilp and Prompoon presented an approach for ranking the items based on user’s reviews. They had considered five pre-defined aspects for the software items. The ranking of the software was computed by comparing the sentences analyzing the different clients’ ratings for every software aspect. This was performed in three phases include gathering user reviews, analyzing the gathered reviews and doing the subsequent software ranking [14].
Zhang et al. proposed a method that utilized the aspect-level sentiment of the users’ reviews with the support of helpfulness reviews [15].
Zheng, Shekhar, Jose, and Rai proposed a multi-criteria decision-making approach in the discipline of educational learning. At first, they integrated the context-awareness and the multi-criteria decision-making in the recommender systems considering the educational data. Their experimental results were quite satisfactory, and it was realized that they were able to produce additional correct suggestions based on two different strategies of recommendations [17].
These are some of the works done by various scientists around the globe. There are thousands of projects which has been conducted or ongoing in the field of MCRS to make the system fully efficient. Nowadays, the leading companies are making using of the recommender systems. One of the best examples is the company Amazon that uses the recommender systems to give proper recommendation to their customers. Netflix is another company that uses multi-criteria recommender system to give a list of movies and web series suggestions to the user on the basis of the user’s details and user’s previous choices. So, day by day, new techniques are being applied in recommender systems to improve the accuracy.
3.3 Working Principle
The most basic question comes in mind that what is recommender system. A recommender system is a software or model that analyze a client’s preference, and based on that, it generates a list of items for that client. A multi-criteria recommender system can be defined as recommender systems that collect information on multiple criteria. The basic working of recommender system is to predict accurate recommendation for a particular user. The recommender system or single-criteria recommender system explores only one criteria and give recommended result. This is the first ever recommender system concept. But for real-world problems, we cannot predict recommendation list by exploring only one criterion at a time. It will give false prediction. So, the MCRS concept comes in the field. These kinds of recommender system can explore multiple-criteria at a time and can give excellent accuracy (Figure 3.1).
Figure 3.1 Working principle of MCRS.
Figure 3.2 Phases of MCRS.
Recommender systems are widely used in e-commerce systems and movie industries and each and every sector. Suppose if we used amazon. com and buy a product, then before check out it shows similar kind of product as add on. This list of items is predicted by amazons very own recommender system. Similarly, if we use Netflix, then we can see that it always recommends new movies and web series to us. This prediction is based on generally two categories, on the basis of our previous choice and other one is on the basis of out Netflix account details. That is how recommender system generates a list which is most suitable to the user.
Every recommender system goes through three types of phases. Those phases are modeling phase, prediction phase, and recommendation phase.
Figure 3.2 explains the different phases of a recommender system. Now, we will see the each phases and their significance.
3.3.1 Modeling Phase
In modeling phase, its focus on preparing the data will be used in next two phase. As we can see in the diagram, it is also divided in three cases. First step is to build a ratio matrix. The rows of the matrix contain the name of the users, and columns contain the items and each cell contains rating, which by the user for a particular item. Now, it generates a user profile. This profile explains the preference of a user. It is mostly a vector and every user has their own private profile of preferences. In the third step, it generates a profile for the items which contains the features of the items.
3.3.2 Prediction Phase
It is the second phase of recommender system. The main objective is to estimate the rating or score of unrevealed or unspecified items for every client. This process is done by a utility function based on the extracted data which is provided by the modeling phase. Different filtering techniques are diagrammatically shown in Figure 3.3.
Figure 3.3 Filtering techniques of MCRS.
3.3.3 Recommendation Phase
This the third phase of recommender system and also extension prediction phase. In this step, various methods are used to hold up clients’ choice by predicting the most acceptable items. As per the user’s interest, new items are recommended in this step.
These are
