This document describes a generic strategy called RAPARE for cold-start rating prediction in recommender systems. It instantiates RAPARE using matrix factorization (RAPARE-MF) and neighborhood-based (RAPARE-KNN) collaborative filtering algorithms. Evaluation on five real datasets shows RAPARE outperforms benchmarks in prediction accuracy and RAPARE-MF provides fast recommendations with linear scalability.

1. RAPARE: A Generic
Strategy for Cold-Start
Rating Prediction
Problem

2. Abstract
RAPARE strategy
We instantiate the proposed
generic RAPARE strategy on
both matrix factorization
based (RAPARE-MF) and
neighborhood based
(RAPARE-KNN) collaborative
filtering, together with
algorithms to solve them.
We present the algorithm
analysis for RAPARE strategy
and its instantiations on
aspects of effectiveness and
efficiency.
In Recent years,
recommender system is one
of indispensable components
in many e-commerce
websites. One of the major
challenges that largely
remains open is the cold-
start problem on your Google
Drive and will be able to edit,
add or delete slides.
Our proposed strategy can
be instantiated into
existing methods I
recommender systems.
2

3. DISADVANTAGE:
The inactive list of products will get
eliminated.
The product vendor and the seller will get
Benefited as every product is recommended
uniformly
3

4. ARCHITECTURE
Let’s start

5. “
5

6. FUTURE
WORK
Improve the evaluations on five real
data sets show that our approach
outperforms several bench marks
collaborative filtering and online
updating methods in terms of
prediction accuracy and RAPARE-MF
can provide fast recommendations
with linear scalability.
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7. REFERENCES
[1] P. Resnick and H. R. Varian, “Recommender systems,”
Communications of the ACM, vol. 40, no. 3, pp. 56–58, 1997.
[2] J. Davidson, B. Liebald, J. Liu, P. Nandy, T. Van Vleet, U. Gargi, S.
Gupta, Y. He, M. Lambert, B. Livingston et al., “The youtube video
recommendation system,” in RecSys. ACM, 2010, pp. 293– 296.
[3] A. S. Das, M. Datar, A. Garg, and S. Rajaram, “Google news
personalization: scalable online collaborative filtering,” in WWW.
ACM, 2007, pp. 271–280.
[4] G. Linden, B. Smith, and J. York, “Amazon. com
recommendations: Item-to-item collaborative filtering,” Internet
Computing, IEEE, vol. 7, no. 1, pp. 76–80, 2003.
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THANKS!
Any questions?
You can find me at 1croreprojects@gmail.com

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