用户对移动网络服务偏好学习技术综述

doi:10.3969/j.issn.1000-436x.2013.02.018

摘要/Abstract

摘要：

摘要：为了缓解日益严重的“移动信息过载问题”，移动用户偏好学习已成为个性化服务领域的首要问题。对最近几年移动网络服务中用户偏好学习技术的研究进展进行综述，对移动用户偏好的表示方法、获取技术、自适应学习方法、评价方法等进行前沿概括、比较和分析。最后对移动网络服务中用户偏好学习技术的发展方向和趋势进行展望。

关键词: 移动网络服务, 移动用户偏好, 偏好获取, 偏好自适应, 个性化服务

Abstract:

Recently,in order to alleviate the problem of “mobile information overload”,the learning of mobile user preferences has become a new hotspot of research in the personalized services.The research progress of learning mobile user preferences were summaried in recent years.The representation,acquisition method,adaptive learni evaluation about learning mobile user preferences were contrasted and analyzed.Finally,some future directions and development trends of learning mobile user preferences in the mobile network service were pointed out.

Key words: mobile network services, mobile user preferences, preference acquisition, preference adaptation, personalized service

孟祥武,史艳翠,王立才,张玉洁. 用户对移动网络服务偏好学习技术综述[J]. 通信学报, 2013, 34(2): 147-155.

Xiang-wu MENG,Yan-cui SHI,Li-cai WANG,Yu-jie ZHANG. Review on learning mobile user preferences for mobile network services[J]. Journal on Communications, 2013, 34(2): 147-155.

参考文献 48

[49]	SYMEONIDIS P , PAPADIMITRIOU A , MANOLOPOULOS Y . Geo-social recommendations based on incremental tensor red ion and local path traversal[A]. Proc of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks[C]. Chicago,Illinois,USA, 2011. 89-96.
[50]	XU B , CHIN A , WANG H . Using physical context in a mobile social networking application for improving friend recommendations[A]. IEEE Int’l Conf on Internet of Things,and Cyber,Phys l and Social Computing[C]. Dalian,China, 2011. 602-609.
[51]	SHI Y C , MENG X W , WANG L C . A heuristic approach to identifying the specific household member for a given rating[A]. ACM CAMRa’11[C]. Chicago,Illinois,USA, 2011. 47-52.
[52]	刘建国, 周涛, 郭强 . 个性化推荐系统评价方法综述[J]. 复杂系统与复杂性科学, 2009,6(3): 1-9. LIU J Q , ZHOU T , GUO Q . Overview of the evaluated algorithms for the personal recommendation systems[J]. JournalComplex Systems and Complexity Science, 2009,6(3): 1-9.
[1]	KWOK R . Phoning in data[J]. Nature, 2009,458(7241): 959-961.
[2]	CHIU P H , KAO G Y M , LO C C . Personalized blog content recommender system for mobile phone users[J]. Int’l Journal of Human-Computer Studies, 2010,68(8): 496-507.
[3]	TUZHILIN A . Personalization:the State of the Art and Future Directions[M]. Business Computing,Handbooks in Information System, 2009. 3-44.
[4]	杜一, 田丰, 戴国忠 ,等. 一种移动环境下的用户模型[J]. 软件学报, 2011,22(2): 120-128. DU Y , TIAN F , DAI G Z ,et al. A user model based on mobile environment[J]. Journal of Software, 2011,22(2): 120-128.
[5]	DO T M T , DANIEL G P . By their apps you shall understand them:mining large-scale patterns of mobile phone usage[A]. Proc of the 13th Int’l Conf on multimodal interfaces[C]. Alicante,Spain, 2011. 353-360.
[6]	JEMBERE E , ADIGUN M O , XULU S S . Mining context-based user preferences for m-Services applications[A]. Proc of the IEEE/WIC/ACM International Conference on Web Intelligence[C]. Silicon Valley,USA, 2007. 57-763.
[7]	SHINTARO O . Exploring gender effects in a mobile advertising context:on the evaluation of trust,attitudes,and recal [J]. Sex Roles, 2007,57: 897-908.
[8]	GOKUL C , JAN B , DANIEL G P . Mining large scale smartphone data for personality studies[J]. Personal and Ubiquitous Computing, 2011.1-18.
[9]	BRDAR S , CULIBRK D , CRNOJEVIC V . Demographic attributes prediction on the real-world mobile data[A]. The Workshop on Mobile Data Challenge 2012 (by Nokia)[C]. Newcastle,UK, 2012.
[10]	LI X Y , MI Z C , ZHAN Z M . A location-aware recommender system for mobile shopping environments[A]. Proc of the 2nd I l Conf on Information Science and Engineering (ICISE)[C]. Hangzhou,China, 2010. 1709-1711.
[11]	CHEN N , CHEN A . Integrating context-aware computing in decision support system[J]. Lecture Notes in Engineering and Computer Science, 2010,2180(1): 359-364.
[12]	QUSAY H M , EYHAB A M , WANG Z X . Design and implementation of a smart system for personalization and accurate selectio of mobile services[J]. Requirements Engineering, 2007,12(4): 221-230.
[13]	YAO X L , SHU H Y . Study on value-added service in mobile telecom based on association rules[A]. Proc of the 10th Int’l f on Software Engineering,Artificial Intelligences,Networking and llel/ Distributed Computing[C]. Dezhou,China, 2009.116-119.
[14]	EAGLE N , PENTLAND A , LAZER D . Inferring social network structure using mobile phone data[J]. Proc of the National Academy of Sciences (PNAS), 2009,106(36): 15274-15278.
[15]	NIKO K , JAN B , OLIVIER D . Towards rich mobile phone datasets:lausanne data collection campaign[A]. Proc of the ACM Int Conf on Pervasive Services (ICPS)[C]. Berlin,Germany, 2010.
[16]	SHI Y C , MENG X W , ZHANG Y J . A trust calculating algorithm based on mobile phone data[A]. Proc of the IEEE Global Telecommunications Conference,(GLOBECOM)[C]. California,USA, 2012.2036-2041.
[17]	CESAR A H , SICKERT C R . The dynamics of a mobile phone network[J]. Physica A:Statistical Mechanics and Its Applications, 2008,387(12): 3017-3024.
[18]	王立才, 孟祥武, 张玉洁 . 移动网络服务中基于认知心理学的用户偏好提取方法[J]. 电子学报, 2011,39(11): 2548-2553. WANG LC , MENG XW , ZHANG YJ . A cognitive psychology-based approach to user preferences elicitation for mobile network services[J]. Acta Electronica Sinica, 2011,39(11): 2548-2553.
[19]	EYHAB A M , QUSAY H M . A context-aware service discovery and selection mechanism using artificial neural networks[J]. Int’l Journal of Intelligent Systems Technologies and Applications, 2009,6(1): 144-156.
[20]	TOON D P , TOM D , LUC M . Extending the bayesian classifier to a context-aware recommender system for mobile devices[A]. Proc of the 2010 5th International Conference on Internet and Web ications and Services [C]. Barcelona, 2010. 242-247.
[21]	MALLIKHARJUNA R N , NAIDU P M M , SEETHARAM P . An intelligent location management approaches in GSM mobi network[J]. International Journal of Advanced Research in Artificial Intelligence, 2012,1(1): 30-37.
[22]	YAN T X , CHU D , GANESAN D . Fast App launching for mobile devices using predictive user context[A]. Proc of the 10th international conference on MobiSys[C]. Lake District,UK, 2012. 113-126.
[23]	LAM D , COX D C , WIDOM J . Teletraffic modeling for personal communications services[J]. IEEE Communications Magazine, 1997,53(2): 79-87.
[24]	KARATZOGLOU A , AMATRIAIN X , BALTRUNAS L . Multiverse recommendation:N-dimensional tensor factorization for contextaware collaborative filtering[A]. Proc of the Recsys[C]. Barcelona,Spain, 2010. 79-86.
[25]	SU J , YEH H , YU P . Music recommendation using content and context information mining[J]. IEEE Intelligent Systems, 2010,5(1): 16-26.
[26]	LINAS B . Context-Aware Collaborative Filtering Recommender Systems[D]. Italy:Free University of Bozen, 2011.
[27]	王玉祥, 乔秀全, 李晓峰 . 上下文感知移动社交网络服务选择机制研究[J]. 计算机学报, 2010,33(11): 2126-2135. WANG Y X , QIAO X Q , LI X F . Research on context awareness mobile SNS service selection mechanism[J]. Chinese Journal of Computers, 2010,33(11): 2126-2135.
[28]	HONG J Y , SUH E H , KIM J Y . Context-aware system for proactive personalized service based on context history[J]. ACM:Expert Systems with Applications, 2009,36(4): 7448-7457.
[29]	KUO M H , CHEN L C , LIANG C W . Building and evaluating a location-based service recommendation system with a preference adjustment mechanism[J]. Expert Systems with Applications, 2009,36(2): 3543-3554.
[30]	王立才, 孟祥武, 张玉洁 . 上下文感知推荐系统研究[J]. 软件学报, 2012,23(1): 1-20. WANG L C , MENG X W , ZHANG Y J . Context-aware recommender systems:a survey of the state-of-the-art and possible extensions[J]. Chinese Journal of Software, 2012,23(1): 1-20.
[31]	STEFANIDIS K , PITOURA E , VASSILIADIS P . Adding context to preferences[A]. Proc of the IEEE 23rd Int’l Conf on Data Engineering[C]. Istanbul,Turkey, 2007. 846-855.
[32]	WU W , JU Y C , LI X . Paraphrase detection on SMS messages in automobiles[A]. Proc of the IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP)[C]. Dallas,TX, 2010. 5326-5329.
[33]	KENTA O , SHINSUKE N , JUN M . Context-aware SVM for context-dependent information recommendation[A]. Proc of the Int’l Conf on Mobile Data Management[C]. Nara,Japan, 2006. 109-112.
[34]	GUI F , MALEK A , NAPHTALI R . A contextualized and personalized approach for mobile search[A]. Proc of the 2009 International Conference on Advanced Information Networking and Applications Workshops[C]. Bradford, 2009. 966-971.
[35]	GOENKA K , ARPINAR IB,NURALM . Mobile web search personalization using ontological user profile[A]. Proc of the 48th Annual Southeast Regional Conference[C]. Oxford:MS,USA, 2010. 1-6.
[36]	刘栋, 孟祥武, 陈俊亮 . 上下文感知系统中的规则生成与匹配算法[J]. 软件学报, 2009,20(10): 2655-2666. LIU D , MENG X W , CHEN J L . Algorithms for rule generation and matchmaking in context-aware system[J]. Journal of Software, 2009,20(10): 2655-2666.
[37]	NIAZI R , QUSAY H M . An ontology-based framework for discovering mobile services[A]. Proc of the 7th Annual Researc Conf on Communication Networks and Services,CNSR'09[C]. Moncton,NB,Canada, 2009. 178-184.
[38]	XU J J , LIAO S Y , LI Q D . Combining empirical experimentation and modeling techniques:a design research approach for personalized mobile advertising applications[J]. Decision Support Sys ms, 2007,44(3): 710-724.
[39]	INDRANIL B , CHEN X . Hybrid models using unsupervised clustering for prediction of customer churn[J]. Journal of Organizational Computing and Electronic Commerce, 2009,19(2): 133-151.
[40]	SUYKENS J A K , VANDEWALLE J . Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999. 293-300.
[41]	史艳翠, 孟祥武, 张玉洁 ,等. 一种上下文移动用户偏好自适应学习方法[J]. 软件学报, 2012,23(11): 2533-2549. SHI Y C , MENG X W , ZHANG Y J . Adaptive learning approach of contextual mobile user preferences[J]. Journal of Software, 2012,23(10): 2533-2549.
[42]	SARAH M , ELIZA P , NICK T . Adapting pervasive environments through machine learning and dynamic personalization[A]. Proc of the International Symposium on Parallel and Distributed Processing with Applications[C]. Sydney,NSW, 2008. 395-402.
[43]	PAZZANI M , BILLSUS D . Learning and revising user profiles:the identification of interesting web sites[J]. Machine Learning, 1997,27: 313-331.
[44]	OURDIA B , LYNDA T L , MOHAND B . Dynamically personalizing search results for mobile users[J]. Lecture Notes in Computer Science, 2009,(5822): 99-110.
[45]	SARAH M , ELIZABETH P , NICK T . Implicit adaptation of user preferences in pervasive systems[A]. 2009 Fourth Inter tional Conference on Systems[C]. Gosier,Guadeloupe, 2009. 56-62.
[46]	WANG L C , MENG X W , ZHANG Y J . New approaches to mood-based hybrid collaborative filtering[A]. ACM CAMRa’10[C]. Barcelona,Spain, 2010. 28-33.
[47]	WANG J J , BHASKAR P . Periodicity based next place prediction[A]. The Workshop on Mobile Data Challenge (by Nokia)[C]. Newcastle,UK, 2012.
[48]	GAO H J , TANG J L , LIU H . Mobile location prediction in spatio-temporal context[A]. The Workshop on Mobile Data Challenge (by Nokia)[C]. Newcastle,UK, 2012.