A Review of Recommender System Strategies for Enhanced Service Recommendation
DOI:
https://doi.org/10.26821/IJSHRE.10.1.2022.100112Keywords:
Recommender systems, service items, collaborative filtering, hybrid filteringAbstract
Recommendation systems have shown to be a powerful tool for filtering and retrieving data regarding user interactions with service items on the internet in particular. These systems have found widespread use in areas such as research, electronic commerce, tourism, social networking, and a variety of other fields. Recommendation systems aid in the filtering of vast amounts of complicated data by allowing users to rate and anticipate their preferences for items that are likely to interest them. As beneficial as recommendation systems are, several design issues exist while creating recommendation systems, such as cold start, sparsity, privacy, robustness, scalability, prediction accuracy novelty, and recommendation diversification, making them less precise and accurate than necessary. A lot of research efforts applying various recommendation methods have been made in time past. Yet, more is still required to meet the information needs of users. This paper provides an extensive study of recommender systems as well as an elaborate assessment of techniques of recommendation that have appeared already in past research endeavours to foster recommendation accuracy, prediction, and sparsity. Also, the authors discuss the benefits and drawbacks of these recommender systems.
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