基于深度学习的个性化网吧游戏推荐
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摘要
与传统推荐模型相比,深度学习可以更好地理解用户需求、项目特征及用户与项目之间的互动性,从而更有效地发现用户和项目之间的匹配关系。将深度神经网络应用于网吧游戏推荐场景,分析用户的个人偏好,根据时间推移兴趣的变化,对用户历史游戏行为记录进行建模训练,为用户提供个性化Top-N游戏推荐。基于深度神经网络设计训练模型,输入层采用对用户历史行为数据处理后的用户偏好向量,隐藏层运用ReLU激活函数的多层网络,输出层则采用逻辑回归的Softmax结构,最终运用带L2规范项的代价函数评估学习到的模型可靠性。在真实数据集下的实验结果表明,随着隐藏层的增加,该方法能明显降低均方根误差,且能提高召回率。
Compared with the traditional recommendation model,deep learning can better understand user needs,project characteristics and user-project interaction,so as to more effectively discover the matching relationship between users and projects. The deep neural network is applied to the recommended scene of the internet bar game,and the user's personal preference is analyzed. According to the change of interest in the time,the game record of the user history is modeled and trained to provide the user with personalized Top-N game recommendation. Based on the deep neural network design training model,the input layer uses the user preference vector processed by the user's historical behavior data,the hidden layer uses the multi-layer network of the Re LU activation function,and the output layer uses the Logical regression Softmax structure,and finally the L2 specification item is used. The cost function evaluates the reliability of the learned model. Experimental results under the real data set show that with the increase of the hidden layer,the method can significantly reduce the root mean square error and improve recall rate.
Compared with the traditional recommendation model,deep learning can better understand user needs,project characteristics and user-project interaction,so as to more effectively discover the matching relationship between users and projects. The deep neural network is applied to the recommended scene of the internet bar game,and the user's personal preference is analyzed. According to the change of interest in the time,the game record of the user history is modeled and trained to provide the user with personalized Top-N game recommendation. Based on the deep neural network design training model,the input layer uses the user preference vector processed by the user's historical behavior data,the hidden layer uses the multi-layer network of the Re LU activation function,and the output layer uses the Logical regression Softmax structure,and finally the L2 specification item is used. The cost function evaluates the reliability of the learned model. Experimental results under the real data set show that with the increase of the hidden layer,the method can significantly reduce the root mean square error and improve recall rate.
引文
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[2]SHANI G,GUNAWARDANA A.Tutorial on applicationoriented evaluation of recommendation systems[J].AICommunications,2013,26(2):225-236.
[3]PROTASIEWICZ J,PEDRYCZ W,KOZOWSKI M,et al.A recommender system of review ers and experts in review ing problems[J].Know ledge-based Systems,2016,106:164-178.
[4]KOHAR M,RANA C.Survey paper on recommendation system[J].International Journal of Computer Science and Information Technologies,2012,3(2):3460-3462.
[5]AZADJALAL M M,MORADI P,ABDOLLAHPOURI A,et al.A trust-aware recommendation method based on Pareto dominance and confidence concepts[J].Knowledgebased Systems,2017,116:130-143.
[6]SHAMBOUR Q,HOURANI M,FRAIHAT S.An item-based multi-criteria collaborative filtering algorithm for personalized recommender systems[J].International Journal of Advanced Computer Science and Applications,2016,7(8):274-279.
[7]ALHAMID M F,RAWASHDEH M,HOSSAIN M A,et al.Tow ards context-aw are media recommendation based on social tagging[J].Journal of Intelligent Information Systems,2016,46(3):499-516.
[8]余凯,贾磊,陈雨强,等.深度学习的昨天、今天和明天[J].计算机研究与发展,2013,50(9):1799-1804.
[9]SCHMIDHUBER J.Deep learning in neural networks:an overview[J].Neural Netw orks,2015,61:85-117.
[10]SZE V,CHEN Y H,YANG T J,et al.Efficient processing of deep neural networks:a tutorial and survey[J].Proceedings of the IEEE,2017,105(12):2295-2329.
[11]田垚,蔡猛,何亮,等.基于深度神经网络和Bottleneck特征的说话人识别系统[J].清华大学学报(自然科学版),2016,56(11):1143-1148.
[12]DAHL G E,YU D,DENG L,et al.Context-dependent pre-trained deep neural netw orks for large-vocabulary speech recognition[J].IEEE Transactions on Audio Speech and Language Processing,2012,20(1):30-42.
[13]DAN C,MEIER U,SCHMIDHUBER J.Multi-column deep neural netw orks for image classification[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEEPress,2012:3642-3649.
[14]邹冲,蔡敦波,赵娜,等.基于SVM-LeNet模型融合的行人检测算法[J].计算机工程,2017,43(5):169-173.
[15]SATO T,TAKANO Y,MIYASHIRO R,et al.Feature subset selection for logistic regression via mixed integer optimization[J].Computational Optimization and Applications,2016,64(3):865-880.
[16]PHAISANGITTISAGUL E.An analysis of the regularization between L2 and dropout in single hidden layer neural network[C]//Proceedings of the 7th International Conference on Intelligent Systems,Modelling and Simulation.Washington D.C.,USA:IEEE Press,2016:174-179.