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RAPARE: A Generic Strategy for Cold-Start Rating Prediction Problem
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 problemon 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. 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. Abstract
DISADVANTAGE: The inactive list of products will get eliminated. The product vendor and the seller will get Benefited as every product is recommended uniformly
ARCHITECTURE Let’s start
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. FUTURE WORK
[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. REFERENCES
THANKS! Any questions? You can find me at 1croreprojects@gmail.com
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