融合深度学习和因子分解机的兴趣点签到预测研究
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摘要
基于位置的社交网络(Location-Based Social Networks,LBSN)为用户提供基于位置的服务,允许移动用户在社交网络中共享各自的位置以及与位置相关的信息。签到预测研究已经成为LBSN的重要且非常具有挑战性的任务。目前的预测技术大部分集中在以用户为中心的签到预测研究,而针对兴趣点签到预测的研究很少。文中主要研究以特定兴趣点为中心的签到预测。由于数据存在极端稀疏性的问题,运用传统的模型很难从数据中挖掘出用户的潜在签到规律。针对以特定兴趣点为中心的签到预测问题,提出了一种结合因子分解机和深度学习的新型网络模型(TSWNN),该模型融合了时间特征、空间特征、天气特征,基于因子分解机的思想处理高维稀疏向量,并采用全连接的隐藏层以挖掘用户在兴趣点的潜在签到行为模式,预测特定兴趣点的签到情况。在两个经典的LBSN数据集Gowalla和Brightkite上的实验结果表明了所提模型的优越性能。
Location-Based Social Networks(LBSN) provides users with location-based services,allowing mobile users to share their location and location-related information in social networks.The research of check-in prediction has become an important and very challenging task in LBSN.Most of the current prediction techniques mainly focus on user-centered check-in studies,while few researches are based on POI-centered.This paper focused on the check-in prediction of POI-centered.Due to the extreme sparseness of data,it is difficult to use the traditional model to dig out users' potential check-in pattern from data.To solve the problem of prediction based on POI-centered,this paper proposed a novel network model(TSWNN) combining factorization machine and deep learning.This model fuses temporal features,spatial features and weather features,takes advantage of the idea of factorization machine to deal with high dimensional sparse vectors and applies fully-connected hidden layer to the model to dig out users' potential check-in pattern and predict users' check-in behavior on specific point of interest.The experimental results on two classical LBSN datasets(Gowalla and Brightkite) show the superior performance of the proposed model.
Location-Based Social Networks(LBSN) provides users with location-based services,allowing mobile users to share their location and location-related information in social networks.The research of check-in prediction has become an important and very challenging task in LBSN.Most of the current prediction techniques mainly focus on user-centered check-in studies,while few researches are based on POI-centered.This paper focused on the check-in prediction of POI-centered.Due to the extreme sparseness of data,it is difficult to use the traditional model to dig out users' potential check-in pattern from data.To solve the problem of prediction based on POI-centered,this paper proposed a novel network model(TSWNN) combining factorization machine and deep learning.This model fuses temporal features,spatial features and weather features,takes advantage of the idea of factorization machine to deal with high dimensional sparse vectors and applies fully-connected hidden layer to the model to dig out users' potential check-in pattern and predict users' check-in behavior on specific point of interest.The experimental results on two classical LBSN datasets(Gowalla and Brightkite) show the superior performance of the proposed model.
引文
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[5] HE J,LI X,LIAO L,et al.Inferring a Personalized Next Point- of-Interest Recommendation Model with Latent Behavior Patterns[C]//Thirtieth AAAI Conference on Artificial Intelligence.AAAI Press,2016:137-143.
[6] FENG S,LI X,ZENG Y,et al.Personalized ranking metric embedding for next new POI recommendation[C]//International Conference on Artificial Intelligence.AAAI Press,2015:2069-2075.
[7] LI Y,ZHENG Y,ZHANG H,et al.Traffic prediction in a bike-sharing system[C]//Sigspatial International Conference on Advances in Geographic Information Systems.ACM,2015.
[8] ZHENG Y,LIU F,HSIEH H P.U-Air:When urban air quality inference meets big data[C]//ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.ACM,2013:1436-1444.
[9] CAO J,XU S,ZHU X,et al.Effective fine-grained location prediction based on user check-in pattern in LBSNs[J].Journal of Network and Computer Applications,2018,108:64-75.
[10] CHANG J,SUN E.Location3:How Users Share and Respondto Location-Based Data on Social[C]//International Conference on Weblogs and Social Media.Barcelona,Catalonia,2011:74-80.
[11] GAO H J,TANG J L,LIU H.Exploring social-historical ties on location-based social networks[C]//Proceedings of the sixth International AAAI Conference on Weblogs and Social Media.Spain,2012:114-121.
[12] GAO H,TANG J,HU X,et al.Exploring temporal effects for location recommendation on location-based social networks[C]//Proceedings of the 7th ACM Conference on Recommender Systems.ACM,2013:93-100.
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[15] ZHANG W,DU T,WANG J.Deep Learning over Multi-field Categorical Data:A Case Study on User Response Prediction[C]//Advances in Information Retrieval-38th European Conference on IR Research.ECIR,2016:45-57.
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[17] BENGIO Y,YAO L,ALAIN G,et al.Generalized Denoising Auto-Encoders as Generative Models [C]//27th Annual Conference on Neural Information Processing Systems.2013:899-907.
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[19] HE X,BOWERS S,CANDELA J Q,et al.Practical Lessons from Predicting Clicks on Ads at Facebook [C]//Proceedings of 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.2014:1-9.
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