1. Deogiri Institute of Engineering and
Management Studies, Aurangabad
Presentation for Continuous Assessment – 1 of
BTCOC503 Machine Learning (Practical) on
Movie Recommendation System
Presented by
36015 Sandesh Sanjay Bandal
Under the Guidance of
Prof. Sughanda Nandedkar Ma'am
17th Dec.2021 | CSE Department | DIEMS
2. Contents
• Need Statement
• Scope of Recommendation
Systems
• Alternate Design
• Design Used
• Data Collection
17th Dec.2021 | CSE Department | DIEMS
3. Need Statement
Providing related content out of
relevant and irrelevant collection
of items to users of online service
providers.
Given a set of users with their
previous ratings for a set of
movies, can we predict the
rating they will assign to a
movie they have not previously
rated.
17th Dec.2021 | CSE Department | DIEMS
4. Scope of Recommendation Systems
Recommender systems help to personalize a
platform and help the user find something
they like.
Many of the largest E-commerce Web sites are
implementing recommender systems to help their
customers find which products to purchase based on
filtering techniques.
5. Scope of Recommendation Systems
From a business standpoint, the more relevant products a
user finds on the platform, the higher their engagement.
This often results in increased revenue for the platform
itself. Various sources say that as much as 35–40% of tech
giants’ revenue comes from recommendations alone.
Movie recommender systems constitute one
of the fastest growing segments of the
Internet economy today
6. Alternate Design
27th Nov.2021 | CSE Department | DIEMS
Content-Based Movie Recommendation Systems
Collaborative Filtering Movie Recommendation
Systems
Hybrid Filtering Movie Recommendation Systems
9. Data Collection
All users rated at least 20 movies
Total Ratings : 10000054
95580 tags applied to 10681 movies by 71567 users
Total Ratings : 10000054
Data Set Source : Kaggle
Data Set Name : Movie Lens
17th Dec.2021 | CSE Department | DIEMS
