Abstract
Recommender systems are widely used to provide users with recommendations based on their preferences. With the ever-growing volume of information online, recommender systems have been a useful tool to overcome information overload. The utilization of recommender systems cannot be overstated, given its potential influence to ameliorate many over-choice challenges. There are many types of recommendation systems with different methodologies and concepts. Various applications have adopted recommendation systems, including e-commerce, healthcare, transportation, agriculture, and media. This paper provides the current landscape of recommender systems research and identifies directions in the field in various applications. This article provides an overview of the current state of the art in recommendation systems, their types, challenges, limitations, and business adoptions. To assess the quality of a recommendation system, qualitative evaluation metrics are discussed in the paper.
Highlights
The internet and modern web services have been increasing within the last few decades; a surplus of information is accessible to everyone [1]
This paper is structured as follows: Section 2 presents the recommendation categories, Section 3 highlights the main challenges in recommendation system (RS), Section 4 explains the different evaluation metrics, Section 5 introduces the business adoptions of RSs, and Section 6 is for the conclusion and future directions
The habituation effect can best be reduced with multi-criteria decision analysis (MCDA) of features of recommending interfaces taking into account their visual intensity, attention represented by fixations measured with eye-tracking and time required to attract attention after a website is loaded
Summary
The internet and modern web services have been increasing within the last few decades; a surplus of information is accessible to everyone [1]. A recommendation system (RS) aims to predict if an item would be useful to a user based on given information [3]. The use of these systems has been steadily growing within the last few years, where they are used in retail and e-commerce firms like eBay and Amazon. RSs have attracted many researchers for the past years, and various literature reviews were conducted, addressing different RSs’ features, algorithms, and challenges [1,9,10,11,12,13,14]. This paper is structured as follows: Section 2 presents the recommendation categories, Section 3 highlights the main challenges in RSs, Section 4 explains the different evaluation metrics, Section 5 introduces the business adoptions of RSs, and Section 6 is for the conclusion and future directions
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