Final Year Project Survey

kettlecatelbowcornerAI and Robotics

Nov 7, 2013 (3 years and 9 months ago)

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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



Students
:


M. Bharath Kumar

99015 [bharath_m@gdit.iiit.net]


P.
B.G. Chandra Shekar 99016 [pbgcs@gdit.iiit.net]


N. Venkatesh
99096 [venkatesh_n@gdit.iiit.net]


Introduction
:




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.
Item
-
based

algorithms avoid this bottleneck by exploring the relationships between items first,
rather than the relationships between users. Recommendations for users
are
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
Field
:




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
-
knit
communi
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.]

pro
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.

Once
the clus
ters are created, predictions f
o
r

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.



Our
Idea
:



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
system.



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
943

potential
users on
1682

movies categorized into different genres.

Each user has rated atleast
20 movies.



References:


1)

Item
-
Based Collaborative Filtering Recommendation Algorithms.

-

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

2)

An Improved Recommendation Algorithm in Collaborative Filtering.

-

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