Advanced Analytics and Deep Learning Models. Группа авторов
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1 *Corresponding author: [email protected]
2 †Corresponding author: [email protected]
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Multi-Criteria–Based Entertainment Recommender System Using Clustering Approach
Chandramouli Das, Abhaya Kumar Sahoo* and Chittaranjan Pradhan
School of Computer Engineering, KIIT Deemed to be University, Odisha, India
Abstract
Multi-criteria recommender systems are such kind of models that are made to give a user-friendly environment to the user. These models are widely used in every sector of the world. Many leading companies are putting effort to make these multi-criteria recommender models effective by introducing new techniques and approaches. In the past, people used to done the recommendation manually. For example, if someone wants to buy something, then they used to ask other people who have bought that particular thing or people who has some knowledge on that thing. To take this process genuine, automatic, and more efficient, the concept of recommender system came. The first recommender system was based on single criteria. That is known as single-criteria recommender system. But in real-world scenario for recommendation, a model needs to look at more than one criterion. So, the multi-criteria recommender concept came in the picture. In this chapter, we will dig into various types of multi-criteria recommender systems. Here, we will see some innovative ideas, approaches, and methods, which are applied to a multi-criteria recommender system more efficient and effective. We will see how these new and innovative approaches give better result compared to the conventional recommender systems. Here, we have talked about so many different approaches of multi-criteria recommendation techniques done by various researchers around the globe. All these researches are done with the real-world datasets to solve the practical problems. We have also chosen five most likely research activities and explained in details how they have conducted their research and got a successful outcome. But before we dive into the hardcore details, we will see that how a recommender system was made. We talked about the various filtering techniques and the core working principles of a recommender system. At the end of the chapter, we have also discussed about the advantages and disadvantages of the recommender systems. Recommender system helps a business to grow higher and higher and also helps to analyze the risks. For these reasons, multi-criteria recommender systems are trending in the market and got high demand.
Keywords: Clustering, entertainment, mean absolute error, multi-criteria, recommender system
3.1 Introduction
In today’s digital age, there is massive amount of information available over the internet; it provides the users with enormous amount of resources or services pertaining to any domain. As the information over the internet rises, the number of resources and options also tend to increase exponentially, causing information overload which eventually creates a lot of confusion among the clients, thus making the decision-making process strenuous [1].
Recommender systems are widely used in the decision-making process and deal with the information overload. Multi-criteria recommendation system is a type of recommender system that utilizes user’s rating and preference on several criteria to make the optimal decision for the respective client. It can thus make a personalized recommendation based on the user’s demands and choices. In this paper, we compare the performance of the recommendation system among three types of settings, first by using the ratings of all the criteria using the traditional approach, second by taking multiple-criteria preference as circumstance, and third by make use of chosen criteria ratings as circumstances. Thus, recommender system is a significant tool used in the decision-making process. It produces a recommendation list items to a client based on the client’s previous likings [28–31].
The importance of recommender systems has been increasing day by day especially for the business applications, as the use of recommender system proved to be quite successful in the ecommerce sector like amazon. Many business applications started incorporating it in variety