Tongda Zhang
ABSTRACT
Online advertising, one typical application of recommendation system, calls for effective and accurate recommendations of keywords. Extreme sparse and large scale data makes online advertising a challenging problem. To achieve better performance and accuracy of the recommendation, a better model with a short turnaround time is needed. In this paper, we address the problem of personalized online advertising for extreme sparse and large scale data. We develop a novel machine learning model (Similarity Powered Pairwise Amplifier Network, SPPAN for short). The complexity of this model (a.k.a. the number of parameters) grows with the amount of observed data, which makes it suitable to extremely sparse data. The training algorithm based on gradient descent makes it easy to parallelize. The similarity model combines the user neighborhood and item neighborhood ideas in collaborative filtering smartly, obtaining a cost-effective way to handle large scale data. The proposed framework is evaluated on a large set of real-world data set from a large internet company (expressed by "Company A"). The experiment results demonstrate that the proposed SPPAN model can greatly improve the prediction and recommendation accuracy on that extreme sparse data set compared with existing approaches.
- P. Resnick, H. R. Varian, Recommender systems. Commun. ACM. 40, 56--58 (1997)]Google Scholar
Digital Library
- B. M. Sarwar, G. Karypis, J. A. Konstan, J. Riedl, Item-based collaborative filtering recommendation algorithms. web Conf., 285--295 (2001)]Google Scholar
- M. Žitnik, B. Zupan, NIMFA: a python library for nonnegative matrix factorization. J. Mach. Learn. Res. 13, 849--853 (2012)]Google ScholarDigital Library
- C. Lin, Projected Gradient Methods for Nonnegative Matrix Factorization. Neural Comput. 19, 2756--2779 (2007)]Google ScholarDigital Library
- D. D. Lee, H. S. Seung, in Advances in neural information processing systems (2001), pp. 556--562]Google Scholar
- J.-P. Brunet, P. Tamayo, T. R. Golub, J. P. Mesirov, Metagenes and molecular pattern discovery using matrix factorization. Proc. Natl. Acad. Sci. 101, 4164--4169 (2004)]Google ScholarCross Ref
- F. Ricci, L. Rokach, B. Shapira, in Recommender systems handbook (Springer, 2011), pp. 1--35]Google ScholarCross Ref
- Y. Koren, R. Bell, in Recommender systems handbook (Springer, 2015), pp. 77--118]Google ScholarCross Ref
- G. Linden, B. Smith, J. York, Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput., 76--80 (2003)]Google Scholar
- X. Cai, M. Bain, A. Krzywicki, W. Wobcke, Y. S. Kim, P. Compton, A. Mahidadia, in Australasian Joint Conference on Artificial Intelligence (Springer, 2010), pp. 476--485]Google Scholar
- Y. Hu, Y. Koren, C. Volinsky, in international conference on data mining (2008), pp. 263--272]Google Scholar
- S. A. P. Parambath, Matrix factorization methods for recommender systems (2013)]Google Scholar
- N. Zheng, Q. Li, S. Liao, L. Zhang, in Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval (ACM, 2010), pp. 737--738]Google Scholar
- Q. Gu, J. Zhou, C. Ding, in Proceedings of the 2010 SIAM international conference on data mining (SIAM, 2010), pp. 199--210]Google ScholarCross Ref
- L. Baltrunas, B. Ludwig, F. Ricci, in Proceedings of the fifth ACM conference on Recommender systems (ACM, 2011), pp. 301--304]Google ScholarDigital Library
- F. Cacheda, V. Carneiro, D. Fernández, V. Formoso, Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Trans. Web. 5, 2 (2011)]Google ScholarDigital Library
- Y. Zhang, B. Cao, D.-Y. Yeung, Multi-domain collaborative filtering. arXiv Prepr. arXiv1203.3535 (2012)]Google Scholar
- N. N. Liu, B. Cao, M. Zhao, Q. Yang, in Proceedings of the Workshop on Context-Aware Movie Recommendation (ACM, 2010), pp. 7--13]Google ScholarDigital Library
Index Terms
- Personalized Recommendation using Similarity Powered Pairwise Amplifier Network
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