[1] SALAKHUTDINOV R, MNIH A. Probabilistic matrix factorization[C]// Proceedings of the 20th International Conference on Neural Information Processing Systems. New York: ACM, 2008: 1257-1264. [2] MA H, YANG H, LYU M R, et al. SoRec: social recommendation using probabilistic matrix factorization[C]// Proceedings of the 17th ACM Conference on Information and Knowledge Management. New York: ACM, 2008: 931-940. [3] YANG B, LEI Y, LIU J, et al. Social collaborative filtering by trust[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(8): 1633-1647. [4] GUO G, ZHANG J, YORKE-SMITH N. TrustSVD: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings[C]// Proceedings of the 29th AAAI Conference on Artificial Intelligence. Menlo Park, CA: AAAI Press, 2015: 123-125. [5] BAO J, ZHENG Y, WILKIE D, et al. Recommendations in location-based social networks: a survey[J]. GeoInformatica, 2015, 19(3): 525-565. [6] KOREN Y, BELL R, VOLINSKY C. Matrix factorization tech-niques for recommender systems[J]. Computer, 2009,42(8): 30-37. [7] SHI Y, LARSON M, HANJALIC A. Collaborative filtering beyond the user-item matrix: a survey of the state of the art and future challenges[J]. ACM Computing Surveys, 2014, 47(1): Article No. 3. [8] MARSDEN P V, FRIEDKIN N E. Network studies of social influence[J]. Sociological Methods & Research, 1993, 22(1): 127-151. [9] KOREN Y. Factorization meets the neighborhood: a multifaceted collaborative filtering model[C]// Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2008: 426-434. [10] 刘华锋,景丽萍,于剑.融合社交信息的矩阵分解推荐方法研究综述[J].软件学报,2018,29(2):340-362.(LIU H F, JING L P, YU J. Survey of matrix factorization based recommendation methods by integrating social information[J]. Journal of Software, 2018,29(2):340-362.) [11] GAO H, LIU H. Mining human mobility in location-based social networks[J]. Synthesis Lectures on Data Mining and Knowledge Discovery, 2015, 7(2): 1-115. [12] LIAN D, ZHAO C, XIE X, et al. GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation[C]// Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2014: 831-840. [13] YE M, YIN P, LEE W C. Location recommendation for location-based social networks[C]// Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York: ACM, 2010: 458-461. [14] 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. New York: ACM, 2016: 975-984. [15] YUAN Q, CONG G, MA Z, et al. Time-aware point-of-interest recommendation[C]// Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2013: 363-372. [16] CHENG C, YANG H, LYU M 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. Menlo Park, CA: AAAI Press, 2013: 2605-2611. [17] LIU B, XIONG H. Point-of-interest recommendation in location based social networks with topic and location awareness[EB/OL].[2018-06-20].https://binbenliu.github.io/papers/poi_sdm13.pdf. [18] 任星怡,宋美娜,宋俊德.基于用户签到行为的兴趣点推荐[J].计算机学报,2017,40(1):28-51.(REN X Y, SONG M N, SONG J D. Point-of-interest recommendation based on the user check-in behavior[J]. Chinese Journal of Computers, 2017,40(1):28-51.) [19] JIANG S, QIAN X, MEI T, et al. Personalized travel sequence recommendation on multi-source big social media[J]. IEEE Transactions on Big Data, 2016, 2(1): 43-56. [20] WANG S, WANG Y, TANG J, et al. What your images reveal: Exploiting visual contents for point-of-interest recommendation[C]// Proceedings of the 26th International Conference on World Wide Web. Geneva: International World Wide Web Conferences Steering Committee, 2017: 391-400. [21] McAULEY J, TARGETT C, SHI Q, et al. Image-based recommendations on styles and substitutes[C]// Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2015: 43-52. [22] WANG Y, WANG S, TANG J, et al. Unsupervised sentiment analysis for social media images[EB/OL].[2018-06-20]. https://ijcai.org/Proceedings/15/Papers/336.pdf. [23] LI X, PHAM T A N, CONG G, et al. Where you instagram?: Associating your instagram photos with points of interest[C]// Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. New York: ACM, 2015: 1231-1240. [24] ZHOU D, HOFMANN T, SCH?LKOPF B. Semi-supervised learning on directed graphs[EB/OL].[2018-06-20].http://papers.nips.cc/paper/2718-semi-supervised-learning-on-directed-graphs.pdf. [25] LI H, HONG R, ZHU S, et al. Point-of-interest recommender systems: a separate-space perspective[C]// Proceedings of the 2015 IEEE International Conference on Data Mining. Piscataway, NJ: IEEE, 2015: 231-240. [26] WANG S, TANG J, WANG Y, et al. Exploring implicit hierarchical structures for recommender systems[C]// Proceedings of the 24th International Conference on Artificial Intelligence. Menlo Park, CA: AAAI Press, 2015: 1813-1819. [27] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J/OL]. arXiv Preprint, 2014, 2014: arXiv:1409.1556(2014-09-04)[2015-04-10]. https://arxiv.org/abs/1409.1556. |