Advanced Analytics and Deep Learning Models. Группа авторов. Читать онлайн. Newlib. NEWLIB.NET

Автор: Группа авторов
Издательство: John Wiley & Sons Limited
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isbn: 9781119792413
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3 Support Vector Machine 0.204336 126.440620 0.795664 20% 4 Random Forest Regressor 0.884247 48.226644 0.115753 88% 5 XGBoost 0.891979 46.588246 0.108021 89%

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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

      Keywords: Clustering, entertainment, mean absolute error, multi-criteria, recommender system

      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