joint representation learning for multi-modal …joint representation learning for multi-modal...
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Joint Representation Learning for Multi-Modal Transportation Recommendation
Hao Liu1, Ting Li2, Renjun Hu3, Yanjie Fu4, Jingjing Gu5, Hui Xiong1∗ 1The Business Intelligence Lab, Baidu Research
2National University of Defense Technology, Changsha, China 3SKLSDE Lab, Beihang University, Beijing, China
4Missouri University of Science and Technology, Missouri, USA 5Nanjing University of Aeronautics and Astronautics, Nanjing, China
Present by: Dr. Hao Liu
Emerging user requirements
High route planning decision cost across multiple transportation modes
Increasing activity radius
Complex travel context
Diversified transportation choices
Personalized and context-aware intelligent route planning Mul$-ModalTransporta$onRecommenda$on
Related Work
RouteRecommenda$on
• Liuetal.[1]discussedgenera$ngmul$-modalshortestroutesbasedonheterogeneoustransporta$onnetwork.
• MPR[2]andTPMFP[3]minesthemostpopularroutesandthemostfrequentpathsfrommassivetrajectoriesontheroadnetwork,respec$vely.
• Rogersetal.[4]considerspersonalpreferencetoimproverouterecommenda$onsquality.
NetworkEmbedding
• Metapath2vec[5]studiesnetworkembeddinginheterogeneousnetworks.
• Yaoetal.[6]andWangetal.[7]leveragesnetworkembeddingforregionfunc$onprofiling.
• Fengetal.[8]andZhaoetal.[9]appliesnetworkembeddingonPOIrecommenda$ons.
Trans2vec: Multi-Modal Transportation Recommendation Architecture
OD profiling
POI KG
User profiling
Multi-modal data
User
Modes
OD
Real time ETA
Station service
User profile
Context sensing
Trans2vec
Multi-modal transportation graph
construction
Joint representation learning
Online recommendations
Multi-Modal Transportation Graph Construction
• Amul&-modal transporta&on graph is a heterogeneous undirectedweighted graph𝐺=(𝑉,𝐸), where 𝑉=𝑈∪𝑂𝐷∪𝑀 is a set of heterogeneous nodes, and 𝐸= 𝐸↓𝑢𝑚 ∪ 𝐸↓𝑜𝑑𝑚 ∪ 𝐸↓𝑢𝑢 ∪ 𝐸↓𝑜𝑑𝑜𝑑 isasetofheterogeneousedgesincludinguser-modeedges 𝐸↓𝑢𝑚 ,OD-modeedges 𝐸↓𝑜𝑑𝑚 ,user-useredges 𝐸↓𝑢𝑢 andOD-ODedges 𝐸↓𝑜𝑑𝑜𝑑 .
Office toIndustrial
CBD to MallResidentialto MallResidential
to Office
Users
Transportmodes
OD pairs
Car
Taxi
BusBike
Walk
Anillustra$veExampleofMul$-modalTransporta$onGraphTravelevents
ResidentialIndustrial
MallCBD
• Analogizetraveleventstosentencesandrandomwalks,inordertolearnlow-dimensionalrepresenta$onsofusers,ODpairs,andtransportmodes.
The Base Model
sigmoid Embedding of user Embedding of mode Embedding of OD
User-mode-ODembedding:
EmbeddingwithNega$vesampling:
Anchor Embedding
Pairwisetransportmoderelevancematrix
Problem
Ø thereareonlyseveral(e.g.,5inourcase)
transportmodenodeswhereastherearea
largenumberofusernodesandODnodes.
ü eachnodeisassignedadiscrimina$ve
embeddingthatreflectsitsinherentcontext
informa$on.
Solution
• Thechoiceoftransportmodeishighlyinfluencedbythecharacteris$csofusers
• e.g.,age,sex,mar$al
• User-userrelevance:
• Userconstraints:
Modeling User Relevance
Beyondtravelpreference:fined-graineduserprofileatBaidu
User attribute vector
• Distanceandtravelpurpose(e.g.,home-work,home-commercial)areamongthemostinfluen$alfactorsforchoosingtransportmodes
• ODrelevence:
• ODconstraints:
Modeling OD Relevance
ODheatmap
Joint Learning & Online Recommendations
• Overallobjec$ve:
• Thescoreofeachmodeiscomputedby:
Experiments – Objectives & Data Sets
Table1.DataSta$s$cs
• TheoverallperformanceofTrans2Vec
• Theparametersensi$vity• Thetransportmoderelevance• TherobustnessofTrans2Vec
Objec$ves• BEIJINGandSHANGHAI• ProducedbasedonthemapqueriesanduserfeedbacksontheBaiduMap,
• TimewindowApril1,2018-August20,2018.
Datasets
Experiments – Overall Results
Table2.Overallperformance
• Logis$cregression• L2R[10]• PTE[11]• Metapath2Vec[5]
• NDCG@5,• Theweightedprecision(PREC)• Recall(REC)• F1
Evalua$onmetrics Baselines
Experiments – Parameter Sensitivity
EffectofdonBEIJING EffectofkonBEIJING
Effectof𝛽↓1 onBEIJING Effectof𝛽↓2 onBEIJING
Experiments – Robustness Check
GroupbyusersonBEIJING GroupbyodsonBEIJING
• Wetesttheperformanceonfoursubgroupsofusers(resp.ODpairs)• Groupusers(resp.ODpairs)byK-means
• TheperformanceisstableindifferentgroupsofusersandODpairs.
%
Faster than bus & drive
%
Cheaper than taxi
Multi-Modal Transportation Recommendation on Baidu Map Multi-Modal Transportation Recommendation on Baidu Map
Multi-Modal Transportation Recommendation on Baidu Map References
[1]Liu,L.2011.Datamodelandalgorithmsformul&modalrouteplanningwithtransporta&onnetworks.Ph.D.Disser-ta$on,TechnischeUniversitatMunchen.[2]Chen,Z.;Shen,H.T.;andZhou,X.2011.Discoveringpopularroutesfromtrajectories.[3]Luo,W.;Tan,H.;Chen,L.;andNi,L.M.2013.Find-ing$meperiod-basedmostfrequentpathinbigtrajectorydata.InProceedingsofthe2013ACMSIGMODinterna-&onalconferenceonmanagementofdata,713–724.ACM.[4]Rogers,S.,andLangley,P.1998.Personalizeddrivingrouterecommenda$ons.InProceedingsoftheAmericanAssoci-a&onofAr&ficialIntelligenceWorkshoponRecommenderSystems,96–100.[5]Dong,Y.;Chawla,N.V.;andSwami,A.2017.metap-ath2vec:Scalablerepresenta$onlearningforheterogeneousnetworks..[6]Yao,Z.;Fu,Y.;Liu,B.;Hu,W.;andXiong,H.2018.Rep-resen$ngurbanfunc$onsthroughzoneembeddingwithhu-manmobilitypaoerns.InIJCAI,3919–3925.[7]Wang,H.,andLi,Z.2017.Regionrepresenta$onlearningviamobilityflow.InProceedingsofthe2017ACMonCon-ferenceonInforma&onandKnowledgeManagement,237–246.ACM.[8]Feng,S.;Cong,G.;An,B.;andChee,Y.M.2017.Poi2vec:Geographicallatentrepresenta$onforpredic$ngfuturevis-itors.InAAAI,102–108.[9]Zhao,S.;Zhao,T.;King,I.;andLyu,M.R.2017.Geo-teaser:Geo-temporalsequen$alembeddingrankforpoint-of-interestrecommenda$on.InProceedingsofthe26thin-terna&onalconferenceonworldwidewebcompanion,153–162.Interna$onalWorldWideWebConferencesSteeringCommioee.[10]Burges,C.J.2010.Fromranknettolambdaranktolamb-damart:Anoverview.Technicalreport.[11]Tang,J.;Qu,M.;andMei,Q.2015.Pte:Predic$vetextem-beddingthroughlarge-scaleheterogeneoustextnetworks.SIGKDD.
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