Final Year Project Survey

kettlecatelbowcornerAI and Robotics

Nov 7, 2013 (4 years and 8 months ago)


Final Year Project Survey

Project Title
Exploring The Effect Of Transitive Similarity On The
Performance Of Item Based Collaborative Filtering

Guiding Professor
P. Krishna Reddy


M. Bharath Kumar

99015 []

B.G. Chandra Shekar 99016 []

N. Venkatesh
99096 []


Collaborative Filtering

works by building a database of preferences for items
by users. A new user, Neo, is matched against the
database to discover neighbors,
which are other users who have historically had similar taste to Neo. Items that the
neighbors like are then recommended to Neo, as he will probably also like them.
The bottleneck in conventional collaborative filtering algo
rithms is the search for
neighbors among a large user population of potential neighbors.

algorithms avoid this bottleneck by exploring the relationships between items first,
rather than the relationships between users. Recommendations for users
computed by finding items that are similar to other items the user has liked.

Because the relationships betwe
en items are relatively static,
and provide the same
quality as the user
based algorithms with

less online computations.

Work Done

In This

Tapestry [Using Collaborative Filtering to Weave an Information Tapestry] is
one of the earliest implementations of collaborative filtering
based recommender
systems. This system relied on the explicit opinions of people from a close
ty, such as an office workgroup. Later, several ratings
based automated
recommender systems were developed. The GroupLens research system [Applying
Collaborative Filtering to Usenet News and
An open Architecture for Collaborative
Filtering of Netnews.]

vides a pseudonymous collaborative filtering solutions for
Usenet news and movies.

Ringo[Information Filtering: Algorithms for Automating
‘Word of Mouth’] and Video Recommender[Recommending and Evaluating Choices
in a virtual Community of Use] are email an
d web based systems that generate
recommendations on music and movies, respectively.

Recommender Systems,
Special Issue of Communications of the ACM presents a number of different
recommender systems.

Other technologies have also been applied to recomm
ender systems, including
Bayesian networks, clustering, and Horting.

Bayesian networks create a model based on a training set with a decision
tree at each node and edges representing user information.

Clustering techniques
work by identifying groups of u
sers who appear to have similar preferences.

the clus
ters are created, predictions f

an individual can be made by averaging the
opinions of the other users in that cluster.

Horting is a graph
based technique in
which nodes are users, and edges betwe
en nodes indicate degree of similarity
between two users

[Horting Hatches an Egg : A New Graph
theoretic Appr
oach to
Collaborative Filtering

Although these systems have been successful in the past, their widespread
use has exposed some of their limitati
ons such as the problems of sparsity in the
data set, problems associated with high dimensionality and so on.


As of now, we made us familiar with the
Item based collaborative filtering
algorithm. Our idea is to extend this algorithm and see t
he effects of the transitive
similarity relationship among the items on the performance of the Recommendation

Data Set:

We got the Movielens Data set and sent a mail to them asking for their
approval in using the Data set.

It consists of the 100
000 ratings of

users on

movies categorized into different genres.

Each user has rated atleast
20 movies.



Based Collaborative Filtering Recommendation Algorithms.


Badrul Sarwar, George karypis, Joseph Konstan, and Jo
hn Riedl.


An Improved Recommendation Algorithm in Collaborative Filtering.


Hun Kim, Young
suk Ryu, Seok
In Park, and Sung
Bong Yang.