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Qualitative Analysis of Models and Issues in Recommender Systems
Published in American Scientific Publishers
Volume: 16
Issue: 5
Pages: 1881 - 1888
A recommender system is an information model that ideally, serves context-aware personalized suggestions and recommendation for an entity that the user might want to use or purchase (e.g., music, movies, and books). This addresses the need to filter and prioritize data into germane information for the user. Traditionally, recommender models follow the two most widely used techniques of Content-Based recommendations and Collaborative filtering. A recommender system should be intuitive and understand contextual references. The model should also exhibit serendipity. Which are too accurate fail in practice since our aim is to generate inclusive and non-intrusive recommendations. This paper presents a roundup of the various recommender models and their prediction techniques, aimed at serving as a basic guideline to the people new to the realm of recommender system and to hopefully empower further research and practice in this field. © 2019 American Scientific Publishers
About the journal
JournalData powered by TypesetJournal of Computational and Theoretical Nanoscience
PublisherData powered by TypesetAmerican Scientific Publishers
Open AccessNo