Abstract
Recommender systems has been wildly used in many websites. These perform much better on users for which they have more information. Satisfying the needs of users new to a system has become an important problem. It is even more accurate considering that some of these hard to describe new users try out the system which unfamiliar to them by their ability to immediately provide them with satisfying recommendations, and may quickly abandon the system when disappointed. Quickly determining user preferences often through a boot process to achieve, it guides users to provide their opinions on certain carefully chosen items or categories. In particular, we advocate a matrix completion solution as the most appropriate tool for this task. We focus on online and offline algorithms that use data compression algorithm and the decision tree which has been built to do real-time recommendation. We merge the three algorithms : distributed matrix completion, cluster-based and decision-tree-based, We chose different algorithms based on different scenarios. The experimental study delivered encouraging results, with the matrix completion bootstrapping process significantly outperforming previous approaches. abstract environment.
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References
Koren, Y.: Factor in the neighbors: scalable and accurate collaborative filtering. ACM Trans. Knowl. Disc. Data (TKDD) 4(1), 1 (2010)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 8, 30–37 (2009)
Rashid, A.M., Albert, I., Cosley, D., et al.: Getting to know you: learning new user preferences in recommender systems. In: Proceedings of the 7th International Conference on Intelligent User Interfaces, pp. 127–134. ACM (2002)
Kohrs, A., Merialdo, B.: Improving collaborative filtering for new users by smart object selection. In: Proceedings of International Conference on Media Features (ICMF) (2001)
Rashid, A.M., Karypis, G., Riedl, J.: Learning preferences of new users in recommender systems: an information theoretic approach. ACM SIGKDD Explor. Newslett. 10(2), 90–100 (2008)
Golbandi, N., Koren, Y., Lempel, R.: On bootstrapping recommender systems. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1805–1808. ACM (2010)
Golbandi, N., Koren, Y., Lempel, R.: Adaptive bootstrapping of recommender systems using decision trees. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 595–604. ACM (2011)
Candes, E., Tao, T.: The Dantzig selector: statistical estimation when p is much larger than n. Ann. Stat. 35(6), 2313–2351 (2007)
James, G.M., Radchenko, P., Lv, J.: DASSO: connections between the Dantzig selector and lasso. J. Roy. Stat. Soc. B (Statistical Methodology) 71(1), 127–142 (2009)
Keshavan, R., Montanari, A., Oh, S.: Matrix completion from noisy entries. In: Advances in Neural Information Processing Systems, pp. 952–960 (2009)
Cands, E.J., Li, X., Ma, Y., et al.: Robust principal component analysis? J. ACM (JACM) 58(3), 11 (2011)
Mackey, L.W., Jordan, M.I., Talwalkar, A.: Divide-and-conquer matrix factorization. In: Advances in Neural Information Processing Systems, pp. 1134–1142 (2011)
Mnih, A., Salakhutdinov, R.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems, pp. 1257–1264 (2007)
Bennett, J., Lanning, S.: The netflix prize. In: Proceedings of KDD Cup and Workshop 2007, p. 35 (2007)
Becker, S.R., Cands, E.J., Grant, M.C.: Templates for convex cone problems with applications to sparse signal recovery. Math. Program. Comput. 3(3), 165–218 (2011)
Chen, P.L.: A linear ensemble of individual and blended models for music rating prediction
Zhou, Y., Wilkinson, D., Schreiber, R., Pan, R.: Large-scale parallel collaborative filtering for the netflix prize. In: Fleischer, R., Xu, J. (eds.) AAIM 2008. LNCS, vol. 5034, pp. 337–348. Springer, Heidelberg (2008)
Gemulla, R., Nijkamp, E., Haas, P.J., et al.: Large-scale matrix factorization with distributed stochastic gradient descent. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 69–77 ACM (2011)
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Pan, B., Xia, ST. (2015). Matrix-Completion-Based Method for Cold-Start of Distributed Recommender Systems. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_67
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DOI: https://doi.org/10.1007/978-3-319-26555-1_67
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