Product Recommendation Algorithms – A Comparative Study
Adeeba Khan
, Pallvi Kumari , Nabin Shah , Himani Tyagi
Recommender Systems (RS), content-based filtering (CBF), recommendation algorithm, collaborative filtering (CBF), hybrid system, recommendation techniques.
Recommendation systems are being widely used in many applications and websites to recommend products and items to buyers or viewers. Recommendation Systems are essential for enhancing user experiences and increasing sales for e-commerce platforms. These systems have become widely popular due to large amount of information at present time and consumer bound environment.
This study reviews, investigates, analyses and compares popular recommendation systems that exist. By conducting a comprehensive analysis, this research seek to identify the strengths and weaknesses of various algorithms, considering factors such as accuracy, efficiency, scalability, and adaptability to different domains.
"Product Recommendation Algorithms – A Comparative Study", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.12, Issue 4, page no.b35-b45, April-2025, Available :https://tijer.org/TIJER/papers/TIJER2504145.pdf
Volume 12
Issue 4,
April-2025
Pages : b35-b45
Paper Reg. ID: TIJER_157472
Published Paper Id: TIJER2504145
Downloads: 000283
Research Area: Others area
Country: Greater Noida, Uttar Pradesh, India
ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: TIJER (IJ Publication) Janvi Wave
