Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Recommender Systems and Techniques · Caching and Content Delivery · Advanced Bandit Algorithms Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Electrical, Computer and Biomedical Engineering Department, Ryerson University, Toronto, ON M5B 2K3, Canada
Computer Science Department, Wilfried Laurier University, Waterloo, ON N2L 3C5, Canada
Funding
Ryerson University
References (90)
Show source-provided references
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