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.

Thanks ! Q & A