用户关系和上下文感知的下一个兴趣点推荐

doi:10.3778/j.issn.1002-8331.2010-0115

摘要/Abstract

摘要： 随着移动设备和社交软件的普遍应用，下一个兴趣点推荐（next POI recommendation）变成了基于位置的社交网络（LBSN）的一个非常重要的任务。现实生活中用户访问的下一个兴趣点通常受到用户签到序列信息、用户关系和该地点的上下文信息等诸多方面的影响。基于循环神经网络（RNN）的方法已经被广泛的应用到下一个兴趣点推荐中，但是这些基于RNN的方法缺乏对用户关系进行深入建模。为了解决上述问题，提出了一种整合用户关系和门控循环单元（GRU）进行下一个兴趣点推荐的模型（GRU-R），同时该模型能够考虑用户签到序列信息、用户关系、兴趣点的时空信息和类别信息等进行下一个兴趣点推荐。在两个真实公开的数据集上进行实验，结果表明提出的模型比现有主流的下一个兴趣点推荐算法具有更高的推荐准确性。

Abstract: With the widespread application of mobile devices and social software, the next point of interest（POI） recommendation has become a very important task of location-based social network（LBSN）. In real life, the next POI is usually affected by the sequence information of user check-in, user relationship and the context information of the location. The methods based on recurrent neural network（RNN） have been widely used in the next POI recommendation, but these methods lack in-depth modeling of user relationship. To solve the above problems, this paper proposes a model（GRU-R） which integrates the user relationship and gated recurrent unit（GRU） to the next POI recommendation（GRU-R）. The proposed model can effectively recommend the next POI by considering user relationships, user history check-in sequence, spatiotemporal information and category information of visited POI. Experiments are conducted on two real public datasets. The experimental results show that the proposed model has higher recommendation accuracy than the existing mainstream next POI recommendation algorithms.

Key words: next POI recommendation, recurrent neural network（RNN）, user relationship, contextual information, gated recurrent unit（GRU）

柴瑞敏, 殷臣. 用户关系和上下文感知的下一个兴趣点推荐[J]. 计算机工程与应用, 2022, 58(7): 197-205.

CHAI Ruimin, YIN Chen. User Relationship and Context-Aware Next Point of Interest Recommendation[J]. Computer Engineering and Applications, 2022, 58(7): 197-205.

参考文献

[1] ZHAO P，ZHU H，LIU Y，et al.Where to go next：A spatio-temporal gated network for next POI recommendation[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence，2019：5877-5884.
[2] CHENG C，YANG H，MICHAEL R，et al.Where you like to go next：Successive point-of-interest recommendation[C]//Proceedings of the 23rd International Joint Conference on Artificial Intelligence，2013：2605-2611.
[3] LIU Q，WU S，WANG L，et al.Predicting the next location：A recurrent model with spatial and temporal contexts[C]//Proceedings of the 30th AAAI Conference on Artificial Intelligence，2016：194-200.
[4] RENDLE S，FREUDENTHALER C，SCHMIDT-THIEME L.Factorizing personalized markov chains for next-basket recommendation[C]//Proceedings of the 19th International Conference on World Wide Web，2010：811-820.
[5] HE J，LI X，LIAO L.Next point-of-interest recommendation via a category-aware listwise Bayesian personalized ranking[J].Journal of Computational Science，2018，28：206-216.
[6] LIU B，XIONG H，PAPADIMITRIOU S，et al.A general geographical probabilistic factor model for point of interest recommendation[J].IEEE Transactions on Knowledge and Data Engineering，2015，27（5）：1167-1179.
[7] ZHANG Z，LI C，WU Z，et al.NEXT：A neural network framework for next POI recommendation[J].Frontiers of Computer Science，2020，14（2）：314-333.
[8] LIAO J，LIU T，LIU M，et al.Multi-context integrated deep neural network model for next location prediction[J].IEEE Access，2018，6：21980-21990.
[9] LONG Y，ZHAO P，SHENG V，et al.Social personalized ranking embedding for next POI recommendation[C]// Proceedings of International Conference on Web Information Systems Engineering，Puschino，Russia，2017：91-105.
[10] LI H，GE Y，HONG R，et al.Point-of-interest recommendations：Learning potential check-ins from friends[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining，2016：975-984.
[11] YE M，YIN P，LEE W C.Location recommendation for location-based social networks[C]//Proceedings of ACM Sigspatial International Symposium on Advances in Geographic Information Systems，2010：458-461.
[12] PEROZZI B，AL-RFOU R，SKIENA S.DeepWalk：Online learning of social representations[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining，New York，America，2014：701-710.
[13] CHENG C，YANG H，KING I，et al.Fused matrix factorization with geographical and social influence in location-based social networks[C]//Proceedings of AAAI Conference on Artificial Intelligence，Toronto，Ontario，Canada，2012.
[14] FENG S，LI X，ZENG Y，et al.Personalized ranking metric embedding for next new POI recommendation[C]//Proceedings of the 24th International Joint Conference on Artificial Intelligence，2015：2069-2075.
[15] XIE M，YIN H，WANG H，et al.Learning graph-based POI embedding for location-based recommendation[C]//Proceedings of the 25th ACM International Conference on Information and Knowledge Management，Indianapolis，America，2016：15-24.
[16] GUO H，TANG R，YE Y，et al.DeepFM：A factorization-machine based neural network for CTR prediction[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence，Melbourne，Australia，2017：1725-1731.
[17] LIU B，TANG R，CHEN Y，et al.Feature generation by convolutional neural network for click-through rate prediction[C]//Proceedings of the World Wide Web Conference，San Francisco，CA，America，2019：1119-1129.
[18] XIN X，CHEN B，HE X，et al.CFM：Convolutional factorization machines for context-aware recommendation[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence，2019：3926-3932.
[19] HOCHREITER S，SCHMIDHUBER J.Long short-term memory[J].Neural Computation，1997，9（8）：1735-1780.
[20] CHUNG J，GüL?EHRE C，CHO K，et al.Empirical evaluation of gatedrecurrent neural networks on sequence modeling[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing，2014：1724-1734.
[21] MIKOLOV T，CHEN K，CORRADA G，et al.Efficient estimation of word representations in vector space[C]//Proceedings of the 1st International Conference on Lear-ning Representations，Scottsdale，Arizona，America，2013.
[22] CHEN X，QIU X，ZHU C，et al.Long short-term memory neural networks for Chinese word segmentation[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing，Lisbon，Portugal，2015：1197-1206.
[23] ROCKT?SCHEL T，GREFENSTETTE E，HERMANN K M，et al.Reasoning about entailment with neural attention[C]//Proceedings of the 4th International Conference on Learning Representations，San Juan，Puerto Rico，2016.
[24] LUO A，ZHAO P，LIU Y，et al.Adaptive attention-aware gated recurrent unit for sequential recommendation[C]//Proceedings of the 24th International Conference，Chiang Mai，Thailand，2019：317-322.
[25] RENDLE S，FREUDENTHALER C，GANTNER Z，et al.BPR：Bayesian personalized ranking from implicit feedback[C]//Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence，2009：452-461.
[26] YANG Z，YANG D，DYER C，et al.Hierarchical attention networks for document classification[C]//Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics：Human Language Technologies，2016：1480-1489.