Movie recommendation system using Collaborative filtering and Content filtering
BAIBHAV KUMAR
, RAJAT TIWARI , SAURABH BHALLA , Dr. P.A. JADHAV
A recommendation system uses various algorithms for giving the most preferable and relevant items to users. The system checks the past behavior of a person and gives similar results that might be likely preferable to users. Suppose a new user visits an e-commerce site, it doesn’t have any past history of the user, so in this scenario how will not show any recommendation to a new user? In this case, the site can recommend the best-selling product, I e. the product which is high in demand. Another conceivable arrangement could be to prescribe the items which would carry the greatest benefit to the business. Three main approaches are used for our recommender systems. One is Content-based i.e they offer generalized recommendations to each and every user, based on movie popularity and/or genre. The System recommends the same movies to users with similar content features. Since each user is different, this approach is considered to be too simple. The basic idea behind this system is that movies that are more popular and critically acclaimed will have a higher probability of being liked by the average audience. Second is Knowledge-based filtering, where we try to profile the user’s interests using information collected, and recommend items based on that profile. The other is collaborative filtering, where we try to group similar users together and use information about the group to make recommendations to the user.
"Movie recommendation system using Collaborative filtering and Content filtering", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.5, Issue 9, page no.179 - 182, September-2020, Available :https://ijsdr.org/papers/IJSDR2009030.pdf
Volume 5
Issue 9,
September-2020
Pages : 179 - 182
Paper Reg. ID: IJSDR_192441
Published Paper Id: IJSDR2009030
Downloads: 000347335
Research Area: Engineering
Country: Pune, Maharashtra, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
