移动用户需求获取技术及其应用
作者:
基金项目:

国家自然科学基金(60872051);北京市教育委员会共建项目专项资助


Mobile User Requirements Acquisition Techniques and Their Applications
Author:
  • MENG Xiang-Wu

    MENG Xiang-Wu

    Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia (Beijing University of Posts and Telecommunications), Beijing 100876, China;School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
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  • WANG Fan

    WANG Fan

    Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia (Beijing University of Posts and Telecommunications), Beijing 100876, China;School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
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  • SHI Yan-Cui

    SHI Yan-Cui

    Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia (Beijing University of Posts and Telecommunications), Beijing 100876, China;School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
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  • ZHANG Yu-Jie

    ZHANG Yu-Jie

    Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia (Beijing University of Posts and Telecommunications), Beijing 100876, China;School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
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  • 摘要
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  • 参考文献 [93]
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    摘要:

    近年来,移动用户需求获取技术已成为移动个性化服务研究领域的热点之一.如何利用移动上下文信息进一步提高移动个性化服务的精确性和实时性,成为移动用户需求获取技术的主要任务.对移动用户需求获取技术研究进展进行综述,并对其关键技术、效用评价、应用实践进行前沿概括、比较和分析,最后,对移动用户需求获取技术有待深入的研究难点和发展趋势进行了展望.

    Abstract:

    Mobile user requirements acquisition techniques, aiming to further improve performance accuracy and real-time by fully utilizing mobile contextual information, have recently become one of the hottest topics in the domain of mobile personalized services. This paper presents an overview of the field of mobile user requirements acquisition techniques, including key techniques, evaluation, and typical applications. The prospects for future development and suggestions for possible extensions are also discussed.

    参考文献
    [1] Pan B, Wang XF, Song E, Lai CF, Chen M. Camspf: Cloud-Assisted mobile service provision framework supporting personalized user demands in pervasive computing environment. In: Proc. of the 9th Int'l Wireless Communications and Mobile Computing Conf. (IWCMC). Sardinia: IEEE, 2013. 649-654.
    [2] Capilla R, Babar MA, Pastor O. Quality requirements engineering for systems and software architecting: Methods, approaches, and tools. Requirements Engineering, 2012,17(4):255-258. [doi: 10.1007/s00766-011-0137-9]
    [3] Yang Q, Chen JL, Meng XW. LBS-Oriented creation method and implementation for telecommunication value-added services. Ruan Jian Xue Bao/Journal of Software, 2009,20(4):965-974 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/ 3192.htm [doi: 10.3724/SP.J.1001.2009.03192]
    [4] Adomavicius G, Tuzhilin A. Context-Aware Recommender Systems. In: Ricci F, Rokach L, Shapira B, Kantor PB, eds. Recommender Systems Handbook. Springer-Verlag, 2011. 217-253. [doi: 10.1007/978-0-387-85820-3_7]
    [5] Ai DX, Zuo H, Yang J. Personalized mobile catering recommender system based on context ontology model and rule inference. Advanced Materials Research, 2013. 708-713. [doi: 10.4028/www.scientific.net/AMR.717.708]
    [6] Ricci F, Nguyen QN. Acquiring and revising preferences in a critique-based mobile recommender system. IEEE Intelligent Systems, 2007,22(3):22-29. [doi: 10.1109/MIS.2007.43]
    [7] Woerndl W, Brocco M, Eigner R. Context-Aware recommender systems in mobile scenarios. Int'l Journal of Information Technology and Web Engineering, 2009,4(1):67-85. [doi: 10.4018/jitwe.2009010105]
    [8] Niazi R, Mahmoud QH. An ontology-based framework for discovering mobile services. In: Proc. of the 7th Annual Communication Networks and Services Research (CNSR 2009). Moncton: IEEE, 2009. 178-184. [doi: 10.1109/CNSR.2009.36]
    [9] Mahmoud QH, Al-Masri E, Wang ZX. Design and implementation of a smart system for personalization and accurate selection of mobile services. Requirements Engineering, 2007,12:221-230. [doi: 10.1007/s00766-007-0051-3]
    [10] Al-Masri E, Mahmoud QH. A context-aware mobile service discovery and selection mechanism using artificial neural networks. In: Proc. of the 8th Int'l Conf. on Electronic Commerce (ICEC 2006). New York: ACM Press, 2006. 594-598. [doi: 10.1145/1151454. 1151467]
    [11] Kim J, Cho Y, Kim S. MOBICORS-Movie: A mobile contents recommender system for movie. In: Proc. of the Int'l Conf. on Electronic Business (ICEB 2004). Beijing: IEEE Computer Society, 2004. 789-794.
    [12] Lee SK, Cho YH, Kim SH. Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations. Information Sciences, 2010,180(11):2142-2155. [doi: 10.1016/j.ins.2010.02.004]
    [13] Lee HJ, Park SJ. MONERS: A news recommender for the mobile Web. Expert Systems with Applications, 2007,32(1):143-150. [doi: 10.1016/j.eswa.2005.11.010]
    [14] Zheng VW, Cao B, Zheng Y, Xie X, Yang Q. Collaborative filtering meets mobile recommendation: A user-centered approach. In: Proc. of the AAAI 2010. Atlanta: AAAI, 2010. 236-241.
    [15] Yu ZW, Zhou XS, Zhang D, Chin CY, Wang X, Men J. Supporting context-aware media recommendations for smart phones. IEEE Pervasive Computing, 2006,5(3):68-75. [doi: 10.1109/MPRV.2006.61]
    [16] Meng XW, Shi YC, Wang LC, Zhang YJ. Review on learning mobile user preferences for mobile network services. Journal on Communications, 2013,34(2):147-155 (in Chinese with English abstract). [doi: 10.3969/j.issn.1000-436x.2013.02.018]
    [17] Huang HQ, Zhang P, Zhang XW. Modeling of user preference based on agent for service selection. Acta Electronica Sinica, 2006,34(12A):2537-2540 (in Chinese with English abstract).
    [18] Wang YX, Qiao XQ, Li XF, Meng LM. Research on context-awareness mobile SNS service selection mechanism. Chinese Journal of Computers, 2010,33(11):2126-2135 (in Chinese with English abstract). [doi: 10.3724/SP.J.1016.2010.02126]
    [19] Huang WH, Meng XW, Wang LC. A collaborative filtering algorithm based on users' social relationship mining in mobile communication network. Journal of Electronics & Information Technology, 2011,33(12):3002-3007 (in Chinese with English abstract).
    [20] Wang LC, Meng XW, Zhang YJ. A cognitive psychology-based approach to user preferences elicitation for mobile network services. Acta Electronica Sinica, 2011,39(11):2547-2553 (in Chinese with English abstract). [doi: 10.1360/jos172518]
    [21] Xu FL, Meng XW, Wang LC. A collaborative filtering recommendation algorithm based on context similarity for mobile users. Journal of Electronics & Information Technology, 2011,33(11):2785-2789 (in Chinese with English abstract). [doi: 10.3724/SP.J. 1146.2011.00384]
    [22] Shi YC, Meng XW, Zhang YJ, Wang LC. Adaptive learning approach of contextual mobile user preferences. Ruan Jian Xue Bao/ Journal of Software, 2012,23(10):2533-2549 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/4228.htm [doi: 10.3724/SP.J.1001.2012.04228]
    [23] Shi YC, Meng XW, Zhang YJ, Xiao M. A trust calculating algorithm based on mobile phone data. In: Proc. of the IEEE Globecom. Anaheim: IEEE, 2012. 2012-2017. [doi: 10.1109/GLOCOM.2012.6503411]
    [24] Xiao M, Meng XW, Shi YC, A circuits merging community discovery algorithm based on mobile user behaviors. Journal of Electronics & Information Technology, 2012,34(10):2369-2374 (in Chinese with English abstract). [doi: 10.3724/SP.J.1146.2012. 00331]
    [25] Qiao XQ, Yang C, Li XF, Chen JL. A trust calculating algorithm based on social networking service users' context. Chinese Journal of Computers, 2011,34(12):2404-2413 (in Chinese with English abstract). [doi: 10.3724/SP.J.1016.2011.02403]
    [26] Xie HT, Meng XW. Intelligent configuration recommendation of context-aware mobile application. In: Proc. of the Globlecom 2011 Workshop on Ubiquitous Computing and Networks. Houston: IEEE Computer Society, 2011. 1263-1268. [doi: 10.1109/ GLOCOMW.2011.6162386]
    [27] Wang LC, Meng XW, Zhang YJ. Context-Aware recommender systems. Ruan Jian Xue Bao/Journal of Software, 2012,23(1):1-20 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/4100.htm [doi: 10.3724/SP.J.1001.2012.04100]
    [28] Rich E. User modeling via stereotypes. Cognitive Science, 1979,3(4):329-354. [doi: 10.1207/s15516709cog0304_3]
    [29] Wahlster W, Kobsa A. User Models in Dialog Systems. Berlin: Springer-Verlag, 1989. 4-34. [doi: 10.1007/978-3-642-83230-7_1]
    [30] Kobsa A. User Modeling: Recent Work, Prospects and Hazards. Human Factors in Information Technology, 1993.
    [31] Lieberman H. Letizia: An agent that assists Web browsing. In: Proc. of the 40th Int'l Joint Conf. on Artificial Intelligence (IJCAI'95). Montreal: Lawrence Erlbaum Associates Ltd., 1995. 924-929.
    [32] Mobasher B, Cooley R, Srivastava J. Automatic personalization based on Web usage mining. Communications of the ACM, 2000, 43(8):l42-151. [doi: 10.1145/345124.345169]
    [33] Pazzani M, Muramatsu J, Billsus D. Syskill&Webert: Identifying interesting Web sites. In: Proc. of the 13th National Conf. on Artificial Intelligence. Menlo Park: AAAI Press, 1996. 54-61.
    [34] Mooney RJ, Roy L. Content-Based book recommending using leafing for text categorization. In: Proc. of the 5th ACM Conf. on Digital Libraries. New York: ACM Press, 2000. 195-204. [doi: 10.1145/336597.336662]
    [35] Xie HT, Meng XW. A personalized information service model adapting to user requirement evolution. Acta Electronica Sinica, 2011,39(3):643-648 (in Chinese with English abstract).
    [36] Shahabi C, Chen YS. An adaptive recommendation system without explicit acquisition of user relevance feedback. Distributed and Parallel Databases, 2003,14(2):173-192. [doi: 10.1023/A:1024888710505]
    [37] Shepherd M, Watters C, Marath AT. Adaptive user modeling for filtering electronic news. In: Proc. of the 35th Annual Hawaii Int'l Conf. on System Sciences. Hawaii: IEEE, 2002. 1180-1188. [doi: 10.1109/HICSS.2002.994040]
    [38] Cheng B, Meng XW, Chen JL. An adaptive user requirements elicitation framework. In: Proc. of the 31st Annual Int'l Computer Software and Applications Conf. Beijing: IEEE Computer Society, 2007. 501-502. [doi: 10.1109/COMPSAC.2007.56]
    [39] Meng XW, Hu X, Wang LC, Zhang YJ. Mobile recommender systems and their applications. Ruan Jian Xue Bao/Journal of Software, 2013,24(1):91-108 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/4292.htm [doi: 10.3724/SP.J. 1001.2013.04292]
    [40] Kwok R. Phoning in data. Nature, 2009,458(7241):959-961. [doi: 10.1038/458959a]
    [41] Pérez IJ, Cabrerizo FJ, Enrique HV. A mobile decision support system for dynamic group decision-making problems. IEEE Trans. on Systems, Man, and Cybernetics—Part A: Systems and Humans, 2010,40(6):1244-1256. [doi: 10.1109/TSMCA.2010.2046732]
    [42] Nguyen QN, Hoang TM, Ta LQT, Van Ta C, Hoang PM. User preferences elicitation and exploitation in a push-delivery mobile recommender system. In: Proc. of the Context-Aware Systems and Applications. Springer-Verlag, 2013. 201-211. [doi: 10.1007/ 978-3-642-36642-0_21]
    [43] Gibson W. Implementing a personalized and location based service for delivering advertisements to android mobile users. European Journal of Innovation and Business, 2013,10(1):1-5.
    [44] Ricci F. Mobile recommender systems. Int'l Journal of Information Technology and Tourism, 2011,12(3):205-231.
    [45] Gupta A, Kalra A, Boston D. MobiSoC: A middleware for mobile social computing applications. Mobile Networks and Applications, 2009,14(1):35-52. [doi: 10.1007/s11036-008-0114-9]
    [46] Jembere E, Adigun MO, Xulu SS. Mining context-based user preferences for m-services applications. In: Proc. of the IEEE/WIC/ ACM Int'l Conf. on Web Intelligence. Fremont: IEEE, 2007. 757-763. [doi: 10.1109/WI.2007.94]
    [47] Liu DR, Tsai PY, Chiu PH. Personalized recommendation of popular blog articles for mobile applications. Information Sciences, 2011,181(9):1552-1572. [doi: 10.1016/j.ins.2011.01.005]
    [48] Park MH, Park HS, Cho SB. Restaurant recommendation for group of people in mobile environments using probabilistic multi- criteria decision making. In: Proc. of the 8th Asia-Pacific Conf. on Computer-Human Interaction, Vol.5068. 2008. 114-122. [doi: 10.1007/978-3-540-70585-7_13]
    [49] Girardello A, Michahelles F. AppAware: Which mobile applications are hot? In: Proc. of the 12th Int'l Conf. on Human Computer Interaction with Mobile Devices and Services. Lisboa: ACM Press, 2010. 431-434. [doi: 10.1145/1851600.1851698]
    [50] Yang WS, Cheng HC, Dia JB. A location-aware recommender system for mobile shopping environments. Expert Systems with Applications, 2008,34(1):437-455. [doi: 10.1016/j.eswa.2006.09.033]
    [51] Park, MH, Hong JH, Cho SB. Location-Based recommendation system using bayesian user's preference model in mobile devices. In: Proc. of the 4th Int'l Conf. on Ubiquitous Intelligence and Computing. Berlin, Heidelberg: Springer-Verlag, 2007. 1130-1139. [doi: 10.1007/978-3-540-73549-6_110]
    [52] Schilit WN. A system architecture for context-aware mobile computing [Ph.D. Thesis]. New York: Columbia University, 1995.
    [53] Chen G, Kotz D. A survey of context-aware mobile computing research. Technical Report, TR2000-381, Hanover: Dartmouth College, 2000.
    [54] Hakkila J, Mantyjarvi J. Collaboration in context-aware mobile phone applications. In: Proc. of the 38th Hawaii Int'l Conf. on System Sciences. Hawaii: IEEE, 2005. 33-39. [doi: 10.1109/HICSS.2005.145]
    [55] Hosseini-Pozveh M, Nematbakhsh M, Movahhedinia N. A multidimensional approach for context-aware recommendation in mobile commerce. Int'l Journal of Computer Science and Information Security, 2009,3(1):86-91. [doi: 10.1504/IJICS.2009.026622]
    [56] Setten MV, Pokraev S, Koolwaaij J. Context-Aware recommendations in the mobile tourist application COMPASS. In: Proc. of the 3rd Int'l Conf. on Adaptive Hypermedia and Adaptive Web-Based Systems. Berlin, Heidelberg: Springer-Verlag, 2004. 515-524. [doi: 10.1007/978-3-540-27780-4_27]
    [57] Lee TQ, Park Y. A time-based approach to effective recommender systems using implicit feedback. Expert Systems with Applications, 2008,34(4):3055-3062. [doi: 10.1016/j.eswa.2007.06.031]
    [58] Yap GE, Tan AH, Pang HH. Discovering and exploiting causal dependencies for robust mobile context-aware recommenders. IEEE Trans. on Knowledge and Data Engineering, 2007,19(7):977-992. [doi: 10.1109/TKDE.2007.1028]
    [59] Tang H, Liao SS, Sun SX. A prediction framework based on contextual data to support mobile personalized marketing. In: Proc. of the Decision Support Systems. 2013. 1-13.
    [60] Choeh JY, Lee HJ. Mobile push personalization and user experience. AI Communications, 2008,21(2):185-193. [doi: 10.3233/AIC- 2008-0435]
    [61] Zhang Z, Lin H, Liu K, Wu D, Zhang G, Lu J. A hybrid fuzzy-based personalized recommender system for telecom products/ services. Information Sciences,2013,235:117-129. [doi: 10.1016/j.ins.2013.01.025]
    [62] B?hmer M, Bauer G. Improving the recommendation of mobile services by interpreting the user's icon arrangement. In: Proc. of the 11th Int'l Conf. on Human-Computer Interaction with Mobile Devices and Services. Bonn: ACM Press, 2009. 15-18. [doi: 10.1145/1613858.1613964]
    [63] Demestichas KP, Koutsorodi AA, Adamopoulou EF, Theologou ME. Modeling user preferences and configuring services in B3G devices. Wireless Networks, 2008,14(5):699-713. [doi: 10.1007/s11276-007-0044-7]
    [64] Goenka K, Arpinar IB, Nural M. Mobile Web search personalization using ontological user profile. In: Proc. of the 48th Annual Southeast Regional Conf. Oxford: ACM Press, 2010. [doi: 10.1145/1900008.1900028]
    [65] Jung JJ. Contextualized mobile recommendation service based on interactive social network discovered from mobile users. Expert Systems with Applications, 2009,36(9):11950-11956. [doi: 10.1016/j.eswa.2009.03.067]
    [66] Lee G, Bauer S, Faratin P, Wroclawski J. Learning user preferences for wireless services provisioning. In: Proc. of the 3rd Int'l Joint Conf. on Autonomous Agents and Multi-agent Systems. Washington: IEEE Computer Society, 2004. 480-487. [doi: 10.1109/ AAMAS.2004.161]
    [67] Hong J, Suh EH, Kim J, Kim S. Context-Aware system for proactive personalized service based on context history. Expert Systems with Applications, 2009,36(4):7448-7457. [doi: 10.1016/j.eswa.2008.09.002]
    [68] De Pessemier T, Deryckere T, Martens L. Extending the Bayesian classifier to a context-aware recommender system for mobile devices. In: Proc. of the 5th Int'l Conf. on Internet and Web Applications and Services. Barcelona: IEEE, 2010. 242-247. [doi: 10. 1109/ICIW.2010.43]
    [69] Xu DJ, Liao SS, Li Q. Combining empirical experimentation and modeling techniques: A design research approach for personalized mobile advertising applications. Decision Support Systems, 2008,44(3):710-724. [doi: 10.1016/j.dss.2007.10.002]
    [70] Kim JK, Kim HK, Oh HY, Ryu YU. A group recommendation system for online communities. Int'l Journal of Information Management, 2010,30(3):212-219. [doi: 10.1016/j.ijinfomgt.2009.09.006]
    [71] Chiu PH, Kao GYM, Lo CC. Personalized blog content recommender system for mobile phone users. Int'l Journal of Human- Computer Studies, 2010,68(8):496-507. [doi: 10.1016/j.ijhcs.2010.03.005]
    [72] Pashtan A, Heusser A, Scheuermann P. Personal service areas for mobile Web applications. IEEE Internet Computing, 2004,8(6): 34-39. [doi: 10.1109/MIC.2004.69]
    [73] Eagle N, Pentland A, Lazer D. Inferring social network structure using mobile phone data. Proc. of the National Academy of Sciences (PNAS), 2009,106(36):15274-15278. [doi: 10.1007/978-0-387-77672-9_10]
    [74] Yan B, Chen G. AppJoy: Personalized mobile application discovery. In: Proc. of the 9th Int'l Conf. on Mobile Systems, Applications, and Services (MobiSys 2011). Bethesda: ACM Press, 2011. 113-126. [doi: 10.1145/1999995.2000007]
    [75] Prete DL, Capra L. DiffeRS: A mobile recommender service. In: Proc. of the 11th Int'l Conf. on Mobile Data Management. Kansas: IEEE Computer Society, 2010. 21-26. [doi: 10.1109/MDM.2010.22]
    [76] Xie X, Lian DF. Mobile social networking and user location. China Computer Federation Communications, 2012,8(5):26-31 (in Chinese with English abstract).
    [77] Mohrehkesh S, Ji SW, Nadeem T, Weigle MC. Demographic prediction of mobile user from phone usage. In: Proc. of the Mobile Data Challenge 2012 (by Nokia) Workshop. Newcastle, 2012. 16-21.
    [78] Yan Z, Zhang P, Deng RH. Truberepec: A trust-behavior-based reputation and recommender system for mobile applications. Personal and Ubiquitous Computing, 2012,16(5):485-506. [doi: 10.1007/s00779-011-0420-2]
    [79] Gaikwad AD, Dharmi RR. Personal approach for mobile search: A review. In: Proc. of the Int'l Conf. on Pattern Recognition, Informatics and Mobile Engineering (PRIME). Salem: IEEE, 2013. 221-224. [doi: 10.1109/ICPRIME.2013.6496476]
    [80] Chang YJ, Liu HH, Chou LD, Chen YW, Shin HY. A general architecture of mobile social network services. In: Proc of the 2007 Int'l Conf. on Convergence Information Technology. Gyeongju: IEEE, 2007. 151-156. [doi: 10.1109/ICCIT.2007.132]
    [81] Kjeldskov J, Paay J. Just-for-Us: A context-aware mobile information system facilitating sociality. In: Proc. of the 7th Int'l Conf. on Human Computer Interaction with Mobile Devices & Services. Salzburg: ACM Press, 2005. 23-30. [doi: 10.1145/1085777. 1085782]
    [82] Kamvar M, Baluja S. The role of context in query input: Using contextual signals to complete queries on mobile device. In: Proc of the 9th Int'l Conf. on Human Computer Interaction with Mobile Devices and Services. Singapore: ACM Press, 2007. 405-412. [doi: 10.1145/1377999.1378046]
    [83] Jones S, Jones M, Deo S. Using key phrases as search result surrogates on small screen devices. Personal Ubiquitous Computing, 2004,8(1):55-68. [doi: 10.1007/s00779-004-0258-y]
    [84] Karlson AK, Robertson GG, Bobbins DC, Czerwinski M, Simth G. FaThumb: A facet-based interface for mobile search. In: Proc. of the SIGCHI Conf. on Human Factors in Computing Systems. Montreal: ACM Press, 2006. 711-720. [doi: 10.1145/1124772. 1124878]
    [85] Horozov T, Narasimhan N, Vasudevan V. Using location for personalized POI recommendations in mobile environments. In: Proc. of the Int'l Symp. on Applications and the Internet. Phoenix: IEEE, 2006. 124-129. [doi: 10.1109/SAINT.2006.55]
    [86] Baltrunas L, Ludwig B, Peer S, Ricci F. Context-Aware places of Interest recommendations for mobile users. Design, User Experience, and Usability—Theory, Methods, Tools and Practice, 2011,6769:531-540. [doi: 10.1007/978-3-642-21675-6_61]
    [87] Balke WT, Kie?ling W, Unbehend C. Personalized services for mobile route planning: A demonstration. In: Proc. of the Extending Database Technology (EDBT 2004). Heraklion: IEEE Computer Society Press, 2004. 771-773. [doi: 10.1109/ICDE.2003.1260863]
    [88] Park S, Kang S, Kim YK. A channel recommendation system in mobile environment. IEEE Trans. on Consumer Electronics, 2006, 52(1):33-39. [doi: 10.1109/TCE.2006.1605022]
    [89] Kwon HJ, Hong KS. Personalized real-time location-tagged contents recommender system based on mobile social networks. In: Proc. of the IEEE Int'l Conf. on Consumer Electronics (ICCE). Las Vegas: IEEE, 2012. 558-559. [doi: 10.1109/ICCE.2012. 6161972]
    [90] Yin H, Sun Y, Cui B, Hu Z, Chen L. Lcars: A location-content-aware recommender system. In: Proc. of the 19th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining. Chicago: ACM Press, 2013. 221-229. [doi: 10.1145/2487575.2487608]
    [91] Bedi P, Agarwal SK. Aspect-Oriented trust based mobile recommender system. Int'l Journal of Computer Information Systems and Industrial Management Applications, 2013,5:354-364.
    [92] van der Heijden H, Kotsis G, Kronsteiner R. Mobile recommendation systems for decision making ‘on the go'. In: Proc. of the Int'l Conf. on Mobile Business. Sydney: IEEE, 2005. 137-143. [doi: 10.1109/ICMB.2005.68]
    [93] Baglioni E, Becchetti L, Bergamini L, Colesanti UM, Filipponi L, Persiano G, Vitaletti A. A lightweight privacy preserving SMS- based recommendation system for mobile users. In: Proc. of the RecSys 2010. Barcelana: ACM Press, 2010. 191-198. [doi: 10.1145/1864708.1864745]
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孟祥武,王凡,史艳翠,张玉洁.移动用户需求获取技术及其应用.软件学报,2014,25(3):439-456

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