Leveraging Recency and Credibility in Recommendation System Using Machine Learning and Deep Learning Approaches

dc.contributor.guide	Surendiran, B
dc.coverage.spatial
dc.creator.researcher	Sujitra at Kanmani, R
dc.date.accessioned	2024-02-26T05:21:54Z
dc.date.available	2024-02-26T05:21:54Z
dc.date.awarded	2023
dc.date.completed	2023
dc.date.registered	2018
dc.description.abstract	Available
dc.description.note	Summary p.95, References p.98-118.
dc.format.accompanyingmaterial	None
dc.format.dimensions
dc.format.extent	xii,121p.
dc.identifier.uri	http://hdl.handle.net/10603/547218
dc.language	English
dc.publisher.institution	Computer Science and Engineering
dc.publisher.place	Puducherry
dc.publisher.university	National Institute of Technology Puducherry
dc.relation	No.of References 171.
dc.rights	university
dc.source.university	University
dc.subject.keyword	Cold Start Problem
dc.subject.keyword	Computer Science
dc.subject.keyword	Deceptive Reviews
dc.subject.keyword	Engineering and Technology
dc.subject.keyword	Readability
dc.subject.keyword	Recency
dc.subject.keyword	RoBERTa
dc.subject.keyword	Shilling Attack
dc.subject.keyword	XLM
dc.title	Leveraging Recency and Credibility in Recommendation System Using Machine Learning and Deep Learning Approaches
dc.title.alternative
dc.type.degree	Ph.D.