1. Sriram Patil (201305532)
Nishit Soni (201002026)
Jiten Goyal (201101040)
Information Retrieval and Extraction (CSE474)
International Institute ofTechnology Hyderabad
Online Social Network Based Object
Recommendation System

2. Index
Concept & Architecture
Challenges
Problems with usual techniques
Our approach
Recommendation relevance criteria
Future Scope

3. Concept & Architecture
Social network based object recommendation
Recommending movies
Social Network: Facebook
Dataset: International Movie Database (IMDB)
Website
Login with
Facebook and
fetch users liked
movies
Server
Spark Web
Framework
Jetty Web Server
MySQL
IMDB Dataset

4. Challenges
Two different sources (Facebook and IMDB)
Sparsity
Even active users may have liked well under 5% percent of the
movies.
Scalability
Billions of users and Millions of movies.
Duplication
As movie ids are different. Have to match the movies with
names.

5. Problems with usual techniques
Nearest Neighbour algorithms require computation that
grows with both the number of users and the number of
movies. With billions of users and millions of movies, a
typical web based recommender system running existing
algorithms will suffer serious scalability problems.
Because of sparsity, a recommender system based on nearest
neighbour algorithm may be unable to make any movie
recommendation for a particular user. As, a result the
accuracy of the recommendations may be poor.
Ever growing data and users set.

6. Our approach
Fetch friends with whom the user has atleast some common
movies. If no common movies, then select all the friends.
Get movies liked by the friends.
For each movie, we get a recommendation score.
Parameters considered while assigning score:
Friends which share some likes with the user
Friends of same gender
Friends of same age group
Movies with same genre
Sort the score and return top “n” movies.

7. Recommendation relevance criteria
As the movies are recommended from a lot of friends, it is
little tricky to figure out if the recommendations are
relevant.
We used two criteria
Movies suggested by Facebook.
Our results are comparable to Facebook movie suggestions. And even
better in some cases.
It is a good recommendation if the user has already watched
that movie.

8. Enhancements
There are some more parameters which can be considered
while ranking a movie
Friend list like “Close Friends”, “Relatives”, etc can be given a
little extra weight.
Actors and directors of the movies can be considered when
ranking.
Similar recommendation systems can be extended to
recommend music, books, etc.