基于事件的社交网络上的双边偏好稳态规划
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摘要
在基于事件的社交网络中,一个经典的问题是为用户规划其感兴趣的事件.现有的工作仅仅考虑用户的喜好,仅从用户的角度出发,为其安排尽可能感兴趣的事件来参加.然而,从事件主办者的角度出发,他们亦希望为事件安排的用户尽可能有更大的影响力,用户的可靠性尽可能高,以保障事件能够顺利开展,并取得预期的效果.本质上来说,基于事件的社交网络上的规划问题是一个双向选择的问题,而现有的所有工作均未从用户和事件的双边偏好考虑问题.因此,提出一种双边偏好稳态规划问题来解决这种双向选择问题.该问题首次提出,因此现有工作中未有相关算法可供解决该问题.对比之前只考虑用户偏好的规划,在考虑用户和事件双边偏好时,面临着问题更复杂、约束条件更多的困难.因此,提出两种基础算法和一种改进算法来高效、高质量地解决这个问题,并用大量的实验验证所提出算法的高效性和有效性.
In event based social networks(EBSNs), a typical problem is to plan interested events to users. Existing work only considers the users' preference to events, and plans the events that they are most possibly interested in. However, from the view of event holders,they also hope the users that are assigned to their events are with high influence and reliability. Consequentially, their events can be held successfully and achieve expected effects. Essentially, the planning problem over EBSNs is a bilateral selection problem. However,existing studies never consider the bilateral preference between events and users. Thus, this study proposes a bilateral preference stable planning problem to solve this bilateral selection problem. Since this study is the first to propose the bilateral preference planning problem,no existing algorithms can solve it. Compared with the existing planning problem which only considers the preference of users, the bilateral preference stable planning problem is more complex and contains more constraints. Thus, two baseline algorithms and two improved algorithms are proposed to efficiently and effectively solve this problem. Finally, extensive experiments are conducted to verify the efficiency and effectiveness of the proposed algorithms.
In event based social networks(EBSNs), a typical problem is to plan interested events to users. Existing work only considers the users' preference to events, and plans the events that they are most possibly interested in. However, from the view of event holders,they also hope the users that are assigned to their events are with high influence and reliability. Consequentially, their events can be held successfully and achieve expected effects. Essentially, the planning problem over EBSNs is a bilateral selection problem. However,existing studies never consider the bilateral preference between events and users. Thus, this study proposes a bilateral preference stable planning problem to solve this bilateral selection problem. Since this study is the first to propose the bilateral preference planning problem,no existing algorithms can solve it. Compared with the existing planning problem which only considers the preference of users, the bilateral preference stable planning problem is more complex and contains more constraints. Thus, two baseline algorithms and two improved algorithms are proposed to efficiently and effectively solve this problem. Finally, extensive experiments are conducted to verify the efficiency and effectiveness of the proposed algorithms.
引文
[1]Liu X,He Q,Tian Y,Lee WC,McPherson J,Han J.Event basedsocial networks:Linking the online and offline social worlds.In:Proc.of the 18th ACM SIGKDD Int’l Conf.on Knowledge Discovery and Data Mining.Beijing:ACM Press,2012.1032-1040.
[2]Cheng Y,Yuan Y,Chen L,Giraud-Carrier C,Wang G.Complexevent-participant planning and its incremental variant.In:Proc.of the 2017 IEEE 33rd Int’l Conf.on Data Engineering.San Diego:IEEE,2017.859-870.
[3]She J,Tong Y,Chen L,Cao CC.Conflict-aware eventparticipantarrangement.In:Proc.of the 2015 IEEE 31st Int’l Conf.on Data Engineering.Seoul:IEEE,2015.735-746.
[4]She J,Tong Y,Chen L,Cao CC.Conflict-aware event-participant arrangement and its variant for online setting.IEEE Trans.on Knowledge and Data Engineering,2016,28(9):2281-2295.
[5]Li K,Lu W,Bhagat S,Lakshmanan LV,Yu C.On social eventorganization.In:Proc.of the 20th ACM SIGKDD Int’l Conf.on Knowledge Discovery and Data Mining.New York:ACM Press,2014.1206-1215.
[6]She J,Tong Y,Chen L.Utility-aware social event-participantplanning.In:Proc.of the 2015 ACM SIGMOD Int’l Conf.on Management of Data.Melbourne:ACM Press,2015.1629-1643.
[7]Li SJ,Zhao HY.Foundation of Economics.Beijing:Tsinghua University Press,2015(in Chinese).
[8]Minkov E,Charrow B,Ledlie J,Teller S,Jaakkola T.Collaborativefuture event recommendation.In:Proc.of the 19th ACM Int’l Conf.on Information and Knowledge Management.Toronto:ACM Press,2010.819-828.
[9]Liao G,Zhao Y,Xie S,Yu PS.An effective latent networksfusion based model for event recommendation in offline ephemeral social networks.In:Proc.of the 22nd ACM Int’l Conf.on Information&Knowledge Management.San Francisco:ACM Press,2013.1655-1660.
[10]Khrouf H,Troncy R.Hybrid event recommendation using linkeddata and user diversity.In:Proc.of the 7th ACM Conf.on Recommender Systems.Hong Kong:ACM Press,2013.185-192.
[11]Sun YC,Chen CC.A novel social event recommendation method based on social and collaborative friendships.In:Proc.of the Int’l Conf.on Social Informatics.Kyoto:Springer-Verlag,2013.109-118.
[12]Chen C,Zhang D,Guo B,Ma X,Pan G,Wu Z.Tripplanner:Personalized trip planning leveraging heterogeneous crowdsourced digitalfootprints.IEEE Trans.on Intelligent Transportation Systems,2015,16(3):1259-1273.
[13]Lu EHC,Chen CY,Tseng VS.Personalized trip recommendation with multiple constraints by mining user check-in behaviors.In:Proc.of the 20th Int’l Conf.on Advances in Geographic Information Systems.Redondo Beach:ACM Press,2012.209-218.
[14]Zhang W,Wang J,Feng W.Combining latent factor model with location features for event-based group recommendation.In:Proc.of the 19th ACM SIGKDD Int’l Conf.on Knowledge Discovery and Data Mining.Chicago:ACM Press,2013.910-918.
[15]Du R,Yu Z,Mei T,Wang Z,Wang Z,Guo B.Predicting Activity Attendance in Event-based Social Networks:Content,Context and Social Influence.Seattle:ACM Press,2014.425-434.
[16]Pham TAN,Li X,Cong G,Zhang Z.A general graph-basedmodel for recommendation in event-based social networks.In:Proc.of the 2015 IEEE 31st Int’l Conf.on Data Engineering.Seoul:IEEE,2015.567-578.
[17]Feng K,Cong G,Bhowmick SS,Ma S.In search of influentialevent organizers in online social networks.In:Proc.of the 2014ACM SIGMOD Int’l Conf.on Management of Data.Snowbird:ACM Press,2014.63-74.
[18]Kempe D,Kleinberg J,Tardos E.Maximizing the spread ofinfluence through a social network.In:Proc.of the 9th ACM SIGKDDInt’l Conf.on Knowledge Discovery and Data Mining.Washington:ACM Press,2003.137-146.
[19]Lappas T,Liu K,Terzi E.Finding a team of experts in social networks.In:Proc.of the 15th ACM SIGKDD Int’l Conf.on Knowledge Discovery and Data Mining.Paris:ACM Press,2009.467-476.
[20]Iwama K,Miyazaki S.A survey of the stable marriage problem and its variants.In:Proc.of the Int’l Conf.on Informatics Education and Research for Knowledge-Circulating Society.Kyoto:IEEE,2008.131-136.
[21]Gusfield D,Irving RW.The Stable Marriage Problem:Structure and Algorithms.Cambridge:MIT Press,1989.
[22]Malhotra VS.On the stability of multiple partner stable marriages with ties.In:Proc.of the European Symp.on Algorithms.Beilin:Springer-Verlag,2004.508-519.
[23]Kujansuu E,Lindberg T,M¨akinen E.The stable room mates problem and chess tournament pairings.Divulgaciones Matemáticas,1999,7(1):19-28.
[24]Qiao SJ,Han N,Zhou JL,Li RH,Jin CQ,Gutierrez LA.SocialMix:A familiarity-based and preference-aware location suggestion approach.Engineering Applications of Artificial Intelligence,2018,68:192-204.
[25]Qiao SJ,Han N,Gao YJ,Li RH,Huang JB,Guo J,Gutierrez LA,Wu XD.A fast parallel community discovery model on complex networks through approximate optimization.IEEE Trans.on Knowledge and Data Engineering,2018,30(9):1638-1651.
[26]Qiao SJ,Han N,et al.TraPlan:An effective three-in-one trajectory prediction model in transportation networks.IEEE Trans.on Intelligent Transportation Systems,2015,16(3):1188-1198.
[27]Qiao SJ,Shen DY,Wang XT,Han N,Zhu W.A self-adaptive parameter selection trajectory prediction approach via hidden Markov models.IEEE Trans.on Intelligent Transportation Systems,2015,16(1):284-296.
[7]李世杰,赵岩红.经济学基础.北京:清华大学出版社,2015.
[2]Cheng Y,Yuan Y,Chen L,Giraud-Carrier C,Wang G.Complexevent-participant planning and its incremental variant.In:Proc.of the 2017 IEEE 33rd Int’l Conf.on Data Engineering.San Diego:IEEE,2017.859-870.
[3]She J,Tong Y,Chen L,Cao CC.Conflict-aware eventparticipantarrangement.In:Proc.of the 2015 IEEE 31st Int’l Conf.on Data Engineering.Seoul:IEEE,2015.735-746.
[4]She J,Tong Y,Chen L,Cao CC.Conflict-aware event-participant arrangement and its variant for online setting.IEEE Trans.on Knowledge and Data Engineering,2016,28(9):2281-2295.
[5]Li K,Lu W,Bhagat S,Lakshmanan LV,Yu C.On social eventorganization.In:Proc.of the 20th ACM SIGKDD Int’l Conf.on Knowledge Discovery and Data Mining.New York:ACM Press,2014.1206-1215.
[6]She J,Tong Y,Chen L.Utility-aware social event-participantplanning.In:Proc.of the 2015 ACM SIGMOD Int’l Conf.on Management of Data.Melbourne:ACM Press,2015.1629-1643.
[7]Li SJ,Zhao HY.Foundation of Economics.Beijing:Tsinghua University Press,2015(in Chinese).
[8]Minkov E,Charrow B,Ledlie J,Teller S,Jaakkola T.Collaborativefuture event recommendation.In:Proc.of the 19th ACM Int’l Conf.on Information and Knowledge Management.Toronto:ACM Press,2010.819-828.
[9]Liao G,Zhao Y,Xie S,Yu PS.An effective latent networksfusion based model for event recommendation in offline ephemeral social networks.In:Proc.of the 22nd ACM Int’l Conf.on Information&Knowledge Management.San Francisco:ACM Press,2013.1655-1660.
[10]Khrouf H,Troncy R.Hybrid event recommendation using linkeddata and user diversity.In:Proc.of the 7th ACM Conf.on Recommender Systems.Hong Kong:ACM Press,2013.185-192.
[11]Sun YC,Chen CC.A novel social event recommendation method based on social and collaborative friendships.In:Proc.of the Int’l Conf.on Social Informatics.Kyoto:Springer-Verlag,2013.109-118.
[12]Chen C,Zhang D,Guo B,Ma X,Pan G,Wu Z.Tripplanner:Personalized trip planning leveraging heterogeneous crowdsourced digitalfootprints.IEEE Trans.on Intelligent Transportation Systems,2015,16(3):1259-1273.
[13]Lu EHC,Chen CY,Tseng VS.Personalized trip recommendation with multiple constraints by mining user check-in behaviors.In:Proc.of the 20th Int’l Conf.on Advances in Geographic Information Systems.Redondo Beach:ACM Press,2012.209-218.
[14]Zhang W,Wang J,Feng W.Combining latent factor model with location features for event-based group recommendation.In:Proc.of the 19th ACM SIGKDD Int’l Conf.on Knowledge Discovery and Data Mining.Chicago:ACM Press,2013.910-918.
[15]Du R,Yu Z,Mei T,Wang Z,Wang Z,Guo B.Predicting Activity Attendance in Event-based Social Networks:Content,Context and Social Influence.Seattle:ACM Press,2014.425-434.
[16]Pham TAN,Li X,Cong G,Zhang Z.A general graph-basedmodel for recommendation in event-based social networks.In:Proc.of the 2015 IEEE 31st Int’l Conf.on Data Engineering.Seoul:IEEE,2015.567-578.
[17]Feng K,Cong G,Bhowmick SS,Ma S.In search of influentialevent organizers in online social networks.In:Proc.of the 2014ACM SIGMOD Int’l Conf.on Management of Data.Snowbird:ACM Press,2014.63-74.
[18]Kempe D,Kleinberg J,Tardos E.Maximizing the spread ofinfluence through a social network.In:Proc.of the 9th ACM SIGKDDInt’l Conf.on Knowledge Discovery and Data Mining.Washington:ACM Press,2003.137-146.
[19]Lappas T,Liu K,Terzi E.Finding a team of experts in social networks.In:Proc.of the 15th ACM SIGKDD Int’l Conf.on Knowledge Discovery and Data Mining.Paris:ACM Press,2009.467-476.
[20]Iwama K,Miyazaki S.A survey of the stable marriage problem and its variants.In:Proc.of the Int’l Conf.on Informatics Education and Research for Knowledge-Circulating Society.Kyoto:IEEE,2008.131-136.
[21]Gusfield D,Irving RW.The Stable Marriage Problem:Structure and Algorithms.Cambridge:MIT Press,1989.
[22]Malhotra VS.On the stability of multiple partner stable marriages with ties.In:Proc.of the European Symp.on Algorithms.Beilin:Springer-Verlag,2004.508-519.
[23]Kujansuu E,Lindberg T,M¨akinen E.The stable room mates problem and chess tournament pairings.Divulgaciones Matemáticas,1999,7(1):19-28.
[24]Qiao SJ,Han N,Zhou JL,Li RH,Jin CQ,Gutierrez LA.SocialMix:A familiarity-based and preference-aware location suggestion approach.Engineering Applications of Artificial Intelligence,2018,68:192-204.
[25]Qiao SJ,Han N,Gao YJ,Li RH,Huang JB,Guo J,Gutierrez LA,Wu XD.A fast parallel community discovery model on complex networks through approximate optimization.IEEE Trans.on Knowledge and Data Engineering,2018,30(9):1638-1651.
[26]Qiao SJ,Han N,et al.TraPlan:An effective three-in-one trajectory prediction model in transportation networks.IEEE Trans.on Intelligent Transportation Systems,2015,16(3):1188-1198.
[27]Qiao SJ,Shen DY,Wang XT,Han N,Zhu W.A self-adaptive parameter selection trajectory prediction approach via hidden Markov models.IEEE Trans.on Intelligent Transportation Systems,2015,16(1):284-296.
[7]李世杰,赵岩红.经济学基础.北京:清华大学出版社,2015.
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