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Such systems, which we refer to as multi-criteria recommender systems, have early demonstrated the potential of applying MCDM methods to facilitate recommendation, in numerous application domains. On the other hand, a comprehensive analysis of existing systems would facilitate their understanding and development. Recommendation systems generally use single criteria ratings that define how good an entity is. For example [14] uses a single 10 star rating for each movie for their recommendations. More recently, multi-criteria recommendation systems have become popular, as evidenced by Yahoo! Movies' recent movie recommender system. Various surveys and the recommended system proposed in our research is defined by the most efficient analytics algorithms that ensure finding adequate candidates for a particular job offer and increase the accuracy of the recommendation, the evaluation of our recommender system will be carried out in an it digital services … Multi-Criteria Knowledge-Based Recommender System for Decision Support in Complex Business Processes Aleksandra Revina† Information and Communication Management Applied Informatics in Technical University of Berlin Berlin, Germany revina@tu-berlin.de Nina Rizun Management Gdansk University of Technology Gdansk, Poland nina.rizun@pg.edu.pl ABSTRACT In this paper, we present a concept of a Additional Key Words and Phrases: Recommender systems, Multi-criteria Ratings, Machine Learning 1. INTRODUCTION The provision of personalized shopping recommendations in online stores has shown to be a valuable way to help customers find interesting items and to consequently increase customer loyalty and sales. Recent studies, for example, showed that using a recommendersystem (RS) can lead The rest of this paper is organized as follows: Section 2 reviews relevant studies associated with cultural differences and multi-criteria in recommender systems. In Section 3, the models for consolidating multiple user preferences and cultural factor are defined, and tensor factorization-based prediction is described. Recently, the incorporation of multiple criteria into traditional single-criterion recommender system has increased the. Recommender system aims to solve the information overload problem by recommending a set of items that are suitable for users. Recently, the incorporation of multiple criteria into traditional single-criterion recommender system has increased the . × Close Log In. Log in Recommendation systems based on deep learning have accomplished magnificent results, but most of these systems are traditional recommender systems that use a single rating. In this work, we introduce a multi-criteria collaborative filtering recommender by combining deep neural network and matrix factorization. 2.1 Multi-objective Recommender System Collaborative filtering algorithm (CF) is a popular recommendation algorithm with wide scope of applications. Currently, CF algorithm faces two challenges, which are cold start problem and sparsity of rating matrix. Previous work has shown that these cri