《计算机应用研究》|Application Research of Computers

融合隐语义和邻域算法的兴趣点推荐模型

Synthetic recommendation model for point-of-interest: fusion latent factor and neighborhood-based algorithm

免费全文下载 (已被下载 次)  
获取PDF全文
作者 吴海峰,张书奎,林政宽,贾俊铖
机构 1.苏州大学 计算机科学与技术学院,江苏 苏州 215006;2.江苏省无线传感网高技术研究重点实验室,南京 210003
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2018)07-1955-05
DOI 10.3969/j.issn.1001-3695.2018.07.007
摘要 随着众多具有传感功能的智能手机和可穿戴设备的普及,基于位置的服务得到了快速发展,其中基于位置的社交网络(location-based social networks,LBSN)逐渐被大多数人所接受,基于位置社交网络可以为人们提供兴趣点推荐服务。为了提供更加精准的兴趣点推荐服务,提出了一种融合的算法模型。通过隐语义分析算法来充分挖掘用户的历史行为,使用基于邻域的方法结合好友和地理位置等因素,然后在统一的框架中融合这两种推荐方式的结果,实现了对用户行为更好的预测。实验结果表明,提出的兴趣点推荐方法拥有较好的准确率和召回率。
关键词 基于位置的社交网络;兴趣点推荐;隐语义;信息融合
基金项目 国家自然科学基金资助项目(61201212)
江苏省自然科学基金资助项目(BK2011376)
江苏省“六大人才高峰”项目(2014-WLW-010)
苏州市融合通信重点实验室项目(SKLCC2013XX)
江苏省产学研前瞻性项目(BY2012114)
软件新技术与产业化协同创新中心部分资助项目
本文URL http://www.arocmag.com/article/01-2018-07-007.html
英文标题 Synthetic recommendation model for point-of-interest: fusion latent factor and neighborhood-based algorithm
作者英文名 Wu Haifeng, Zhang Shukui, Lin Zhengkuan, Jia Juncheng
机构英文名 1.SchoolofComputerScience&Technology,SoochowUniversity,SuzhouJiangsu215006,China;2.JiangsuHighTechnologyResearchKeyLaboratoryforWirelessSensorNetworks,Nanjing210003,China
英文摘要 Recently, with the popularity of wearable devices and smart phones which have sensing capabilities, mobile positioning technique is gradually mature. Meanwhile, location-based services have been developed rapidly and the social network included in it, called location-based social networks (LBSN), has gradually been accepted by most people. It can provide point of interest recommendation service. In order to provide a more accurate point of interest recommended service, this paper presented a fusion algorithm model. It excavated the previous behaviors of users sufficiently through latent factor algorithm, and used the neighborhood-based algorithm considering other factors such as friends and geographical position. And then fused results of this two recommended ways based on the unified framework which achieved a better prediction of user behavior. The experimental results show that the point of interest recommendation method has better precision and recalling rate.
英文关键词 LBSN; point of interest recommendation; latent factor; data fusion
参考文献 查看稿件参考文献
  [1] Zheng Yu. Location-based social networks:users[M] //Computing with Spatial Trajectories. New York:Springer, 2011:243-276.
[2] 翟红生, 于海鹏. 在线社交网络中的位置服务研究进展与趋势[J] . 计算机应用研究, 2013, 30(11):3221-3227.
[3] Zhou Dequan, Wang Bin, Rahimi S M, et al. A study of recommending locations on location-based social network by collaborative filtering[C] //Proc of the 25th Canadian Conference on Artificial Intelligence. Berlin:Springer-Verlag, 2012:255-266.
[4] Yu Zhiwen, Tian Miao, Wang Zhu, et al. Shop-type recommendation leveraging the data from social media and location-based services[J] . ACM Trans on Knowledge Discovery from Data, 2016, 11(1):1-9.
[5] Zhao Guoshuai, Qian Xueming, Kang Chen. Service rating prediction by exploring social mobile users’ geographical locations[J] . IEEE Trans on Big Data, 2017, 3(1):67-78.
[6] He Tieke, Yin Hongzhi, Chen Zhenyu, et al. A spatial-temporal topic model for the semantic annotation of POIs in LBSNs[J] . ACM Trans on Intelligent Systems and Technology, 2016, 8(1):12-19.
[7] Yuan Quan, Cong Gao, Ma Zongyang, et al. Time-aware point-of-interest recommendation[C] //Proc of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM Press, 2013:363-372.
[8] Yuan Quan, Cong Gao, Sun Aixin. Graph-based point-of-interest recommendation with geographical and temporal influences[C] //Proc of the 23rd ACM International Conference on Conference on Information and Knowledge Management. New York:ACM Press, 2014:659-668.
[9] Nunes I, Marinho L. A personalized geographic-based diffusion model for location recommendations in LBSN[C] //Proc of the 9th Latin American Web Congress. Washington DC:IEEE Computer Society, 2014:59-67.
[10] Pan Guo, Xu Yuming. Friends prediction based on fusion of topology and location in LBSN[J] . Computer Science, 2014, 9:24.
[11] Bagci H, Karagoz P. Context-aware friend recommendation for location based social networks using random walk[C] //Proc of the 25th International Conference Companion on World Wide Web. 2016:531-536.
[12] Su Chengcheng, Yu Yaxin, Sui Mingfei, et al. Friend recommendation algorithm based on user activity and social trust in LBSNs[C] //Proc of the 12th Web Information System and Application Conference. Piscataway, NJ:IEEE Press, 2015:15-20.
[13] Bagci H, Karagoz P. Random walk based context-aware activity recommendation for location based social networks[C] //Proc of IEEE International Conference on Data Science and Advanced Analytics. Piscataway, NJ:IEEE Press, 2015:1-9.
[14] Xu Huang, Yu Zhiwen, Feng Yun, et al. LBSN-based personalized travel package recommendation system[J] . Computer & Modernization, 2014(1):186-191.
[15] Herlocker J L, Konstan J A, Borchers A, et al. An algorithmic framework for performing collaborative filtering[C] //Proc of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM Press, 1999:230-237.
[16] Ye Mao, Yin Peifeng, Lee W C, et al. Exploiting geographical influen-ce for collaborative point-of-interest recommendation[C] //Proc of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM Press, 2011:325-334.
[17] Wang Jingjin, Lin Kunhui, Li Jia. A collaborative filtering recommendation algorithm based on user clustering and slope one scheme[C] //Proc of the 8th International Conference on Computer Science & Education. Piscataway, NJ:IEEE Press, 2013:1473-1476.
[18] Cheng Chen, Yang Haiqin, King I, et al. Fused matrix factorization with geographical and social influence in location-based social networks[C] //Proc of the 26th AAAI Conference on Artificial Intelligence. 2012:17-23.
[19] Hu Longke, Sun Aixin, Liu Yong. Your neighbors affect your ratings:on geographical neighborhood influence to rating prediction[C] //Proc of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval. 2014:345-354.
[20] 曹玖新, 董羿, 杨鹏伟, 等. LBSN中基于元路径的兴趣点推荐[J] . 计算机学报, 2016, 39(4):675-684.
[21] Xu Guandong, Fu Bin, Gu Yanhui. Point-of-interest recommendations via a supervised random walk algorithm[J] . IEEE Intelligent Systems, 2016, 31(1):15-23.
[22] Pan Rong, Zhou Yunhong, Cao Bin, et al. One-class collaborative filtering[C] //Proc of the 8th IEEE International Conference on Data Mining. Washington DC:IEEE Computer Society, 2008:502-511.
[23] Distance calculation algorithms[EB/OL] . (2013-05-08). http://www. ga. gov. au/earth-monitoring/geodesy/geodetic-techniques/distance-calculation-algorithms. html.
[24] http://snap. stanford. edu/data/loc-gowalla. html[EB/OL] .
[25] 娄超. 基于位置的社交网络中高效的地点推荐方法研究[D] . 杭州:浙江大学, 2012.
[26] 张淼. 基于位置社交网络的兴趣点推荐方法研究[D] . 重庆:西南大学, 2015.
收稿日期 2017/3/20
修回日期 2017/5/18
页码 1955-1959
中图分类号 TP301
文献标志码 A