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简介：Thewidespreadoflocation-basedsocialnetworksbringsaboutahugevolumeofusercheck-indata,whichfacilitatestherecommendationofpointsofinterest（POIs）.Recentadvancesondistributedrepresentationshedlightonlearninglowdimensionaldensevectorstoalleviatethedatasparsityproblem.CurrentstudiesonrepresentationlearningforPOIrecommendationembedbothusersandPOIsinacommonlatentspace,andusers＇preferenceisinferredbasedonthedistance/similaritybetweenauserandaPOI.SuchanapproachisnotinaccordancewiththesemanticsofusersandPOIsastheyareinherentlydifferentobjects.Inthispaper,wepresentanoveltranslation-based,timeandlocationaware（TransTL）representation,whichmodelsthespatialandtemporalinformationasarelationshipconnectingusersandPOIs.Ourmodelgeneralizestherecentadvancesinknowledgegraphembedding.Thebasicideaisthattheembeddingofa〈time,location〉paircorrespondstoatranslationfromembeddingsofuserstoPOIs.SincethePOIembeddingshouldbeclosetotheuserembeddingplustherelationshipvector,therecommendationcanbeperformedbyselectingthetop-kPOIssimilartothetranslatedPOI,whichareallofthesametypeofobjects.Weconductextensiveexperimentsontworeal-worlddata.sets.TheresultsdemonstratethatourTransTLmodelachievesthestate-of-the-artperformance.Itisalsomuchmorerobusttodatasparsitythanthebaselines.

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