Computer Science ›› 2016, Vol. 43 ›› Issue (9): 71-76.doi: 10.11896/j.issn.1002-137X.2016.09.013

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Adaptive Parameters Updating Strategy of Context-aware Factorization Machines

YAO Xing, ZHU Fu-xi, YANG Xiao-lan, ZHENG Lin and LIU Shi-chao   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Context-aware factorization machine has been successfully applied in the context-aware recommendation system.In the learning algorithm of factorization machines,alternating least-squares is a learning algorithm that fixes other parameters just to find the optimal value of a single parameter,and the number of parameters and the sample size will affect the computational complexity.However,when the number of features is large,the number of parameters increases along with the increase of the number of features,resulting in high computational complexity.Even though some parame-ters have achieved the optimal value,all parameters will be updated in each iteration.This paper mainly improved the para-meters updating strategy of alternating least-squares.Adaptive error index was introduced into the parameter.Updating the parameter or not is co-determined by the weights and the absolute error of parameters,so that each iteration focuses on parameters whose last two iterative values change greatly.This strategy only updates parameters whose adaptive errors are greater than the thresholds.It not only reduces the number of parameters that need to be updated,so as to accelerate the algorithm convergence speed and shorten the operation time,but also the weight of parameters is determined by the error,to correct the error.The results of experiments on Yahoo and Movielens data sets show that the effect of the improved parameter updating strategy is better.

Key words: Factorization machines,Alternating least-squares,Recommender systems,Adaptive error

[1] Sarwar B,Karypis G,Konstan J,et al.Item-based collaborativefiltering recommendation algorithms[C]∥Proc International Conference on the World Wide Web.ACM,2001:285-295
[2] Breese J,S D H,Kadie C.Empirical Analysis of Predictive Algo-rithms for Collaborative Filtering[C]∥Proc Conference on Uncertainty in Artificial Intelligence.1998:43-52
[3] Paterek A,Paterek A.Improving regularized singular value decomposition for collaborative filtering[C]∥Proceedings of Kdd Cup & Workshop.2007:58
[4] Srebro N,Rennie J D M,Jaakola T S.Maximum-Margin Matrix Factorization[J].Advances in Neural Information Processing Systems,2004,37(2):1329-1336
[5] Koren Y.Collaborative filtering with temporal dynamics[J].Communications of the ACM,2010,53(4):89-97
[6] Chih-Wei H,Chang C C,Lin C J.A practical guide to support vector classification[D].National Taiwan University,2010
[7] Klema V,Laub A J.The singular value decomposition:Its computation and some applications[J].IEEE Transactions on Automatic Control,1980,25(2):164-176
[8] Karatzoglou A,Amatriain X,Oliver N,et al.Multiverse recommendation:n-dimensional tensor factorization for context-aware collaborative filtering[C]∥Proceedings of the Fourth ACM Conference on Recommender Systems.2010:79-86
[9] Steffen R.Factorization machines [C]∥Proceedings of the 10th IEEE International Conference on Data Mining.IEEE Computer Society,2010:995-1000
[10] Cheng C,Xia F,Zhang T,et al.Gradient boosting factorization machines[C]∥Proceedings of the 8th ACM Conference on Re-commender Systems.ACM,2014:265-272
[11] Friedman J H.Greedy Function Approximation:A GradientBoosting Machine[J].Annals of Statistics,2000,29(5):1189-1232
[12] Riccardi A,Fernandez-Navarro F,Carloni S.Cost-sensitive AdaBoost algorithm for ordinal regression based on extreme learning machine[J].Cybernetics IEEE Transactions on,2014,44(10):1898-1909
[13] Yu L,Liu H.Feature selection for high-dimensional data:a fast correlation-based filter solution[C]∥Proceedings of International Conferences on Machine Learning.2003:856-863
[14] H P,F L,C D.Feature selection based on mutual information:criteria of max-dependency,max-relevance,and min-redundancy[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2005,27(8):1226-1238
[15] Steffen R.Learning recommender systems with adaptive regularization[C]∥Fifth ACM International Conference on Web Search & Data Mining.2012:133-142
[16] Steffen R,Zeno G,Christoph F,et al.Fast context-aware recommendations with factorization machines[C]∥Proceedings of the 34th ACM SIGIR Conference on Research and Development in Information Retrieval.ACM,2011:635-644
[17] Steffen R.Scaling Factorization Machines to Relational Data[C]∥Proceedings of the 39th International Conference on Very Large Data Bases (VLDB 2013).Trento,Italy,2013:337-348
[18] Christoph F, Lars S T, Steffen R.Bayesian Factorization Ma-chines[C]∥Workshop on Sparse Representation and Low-rank Approximation,Neural Information Processing Systems (NIPS-WS).Granada,Spain,2011
[19] Cover T,Hart P.Nearest neighbor pattern classification[J].IEEE Transactions on Information Theory,1967,13(1):21-27
[20] Steffen R.Factorization Machines with libFM[J].ACM Transactions on Intelligent Systems and Technology,2012,3(3):451-458

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