This paper reviews the research trends that link the advanced technical aspects of recommendation systems that are used in various service areas and the business aspects of these services. First, for a reliable analysis of recommendation models for recommendation systems, data mining technology, and related research by application service, more than 135 top-ranking articles and top-tier conferences published in Google Scholar between 2010 and 2021 were collected and reviewed. Based on this, studies on recommendation system models and the technology used in recommendation systems were systematized, and research trends by year were analyzed. In addition, the application service fields where recommendation systems were used were classified, and research on the recommendation system model and recommendation technique used in each field was analyzed. Furthermore, vast amounts of application service-related data used by recommendation systems were collected from 2010 to 2021 without taking the journal ranking into consideration and reviewed along with various recommendation system studies, as well as applied service field industry data. As a result of this study, it was found that the flow and quantitative growth of various detailed studies of recommendation systems interact with the business growth of the actual applied service field. While providing a comprehensive summary of recommendation systems, this study provides insight to many researchers interested in recommendation systems through the analysis of its various technologies and trends in the service field to which recommendation systems are applied.

/pdf/a-survey-of-recommendation-systems-recommendation-models-3jagqqzw.pdf

A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields

Abstract Recommender systems are efficient tools for filtering online information, which is widespread owing to the changing habits of computer users, personalization trends, and emerging access to the internet. Even though the recent recommender systems are eminent in giving precise recommendations, they suffer from various limitations and challenges like scalability, cold-start, sparsity, etc. Due to the existence of various techniques, the selection of techniques becomes a complex work while building application-focused recommender systems. In addition, each technique comes with its own set of features, advantages and disadvantages which raises even more questions, which should be addressed. This paper aims to undergo a systematic review on various recent contributions in the domain of recommender systems, focusing on diverse applications like books, movies, products, etc. Initially, the various applications of each recommender system are analysed. Then, the algorithmic analysis on various recommender systems is performed and a taxonomy is framed that accounts for various components required for developing an effective recommender system. In addition, the datasets gathered, simulation platform, and performance metrics focused on each contribution are evaluated and noted. Finally, this review provides a much-needed overview of the current state of research in this field and points out the existing gaps and challenges to help posterity in developing an efficient recommender system. 

/pdf/a-systematic-review-and-research-perspective-on-recommender-1elyl9a4.pdf

A systematic review and research perspective on recommender systems

User based Collaborative Filtering using fuzzy C-means

An effective collaborative movie recommender system with cuckoo search

Genetic algorithms: theory, genetic operators, solutions, and applications

Recommendation systems have become prevalent in recent years as they dealing with the information overload problem by suggesting users the most relevant products from a massive amount of data. For media product, online collaborative movie recommendations make attempts to assist users to access their preferred movies by capturing precisely similar neighbors among users or movies from their historical common ratings. However, due to the data sparsely, neighbor selecting is getting more difficult with the fast increasing of movies and users. In this paper, a hybrid model-based movie recommendation system which utilizes the improved K-means clustering coupled with genetic algorithms (GAs) to partition transformed user space is proposed. It employs principal component analysis (PCA) data reduction technique to dense the movie population space which could reduce the computation complexity in intelligent movie recom-mendation as well. The experiment results on Movielens dataset indicate that the proposed approach can provide high performance in terms of accuracy, and generate more reliable and personalized movie recommendations when compared with the existing methods. Proposed an optimized clustering algorithm to partition user profiles.To represent denser profile vectors by principal component analysis transforming.Be examined to provide high prediction accuracy and more reliable recommendations.

/pdf/an-improved-collaborative-movie-recommendation-system-using-51cuugtkae.pdf

An improved collaborative movie recommendation system using computational intelligence

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An improved collaborative movie recommendation system using computational intelligence