【基于用戶軌跡的興趣研究的國(guó)內(nèi)外文獻(xiàn)綜述4000字】

基于用戶軌跡的興趣推薦研究的國(guó)內(nèi)外文獻(xiàn)綜述本文主要根據(jù)用戶的行為軌跡來研究用戶的下一個(gè)興趣點(diǎn)推薦方法，具體可分為用戶行為預(yù)測(cè)分析和下一個(gè)興趣點(diǎn)推薦方法這兩個(gè)方面。下面將從這兩個(gè)方面來闡述己有的相關(guān)工作。1.1興趣點(diǎn)推薦早期對(duì)POI推薦的研究主要集中在使用協(xié)同過濾(CF)估計(jì)用戶偏好，特別是基于矩陣分解(MF)的技術(shù)[2-5]。這些方法只能對(duì)用戶的靜態(tài)首選項(xiàng)建模。例如，當(dāng)居住在紐約的用戶到夏威夷度假時(shí)，這些類型的推薦器可能仍然推薦位于紐約的POIs，因?yàn)樗麄儫o(wú)法捕獲用戶偏好的動(dòng)態(tài)。最近，基于深度學(xué)習(xí)的方法，如嵌入學(xué)習(xí)[6-8]，神經(jīng)協(xié)同過濾[9-10]、潛在因子模型和度量學(xué)習(xí)模型[11-12]在許多推薦系統(tǒng)中都取得了很好的性能。Cheng等人[13]的開創(chuàng)性的提出了一種嵌入個(gè)性化馬爾科夫鏈和局部區(qū)域的矩陣分解方法。受RNN在順序數(shù)據(jù)建模中的成功啟發(fā)，基于RNN的方法在next-POI推薦領(lǐng)域變得非常普遍。例如，ST-RNN模型擴(kuò)展了RNN來建模局部時(shí)間和空間上下文。CARA通過利用GRU的gate機(jī)制捕捉用戶的動(dòng)態(tài)偏好。TMCA和STGN分別采用基于LSTM和門控LSTM框架學(xué)習(xí)時(shí)空上下文。DeepMove設(shè)計(jì)了一種多模態(tài)RNN來捕捉順序轉(zhuǎn)移。以上這些方法的提出，極大的擴(kuò)展了人們解決興趣點(diǎn)推薦問題的思路，并為未來相關(guān)問題的解決打下基礎(chǔ)。1.2下一個(gè)興趣點(diǎn)推薦興趣點(diǎn)推薦已經(jīng)吸引了很多產(chǎn)業(yè)界和學(xué)術(shù)界的研究。下一個(gè)興趣點(diǎn)推薦是一般的興趣點(diǎn)的延伸，根據(jù)用戶的歷史簽到信息給用戶推薦將要訪問的下一個(gè)興趣點(diǎn)列表，下一個(gè)興趣點(diǎn)推薦經(jīng)常會(huì)被看成序列推薦問題。目前已經(jīng)有很多方法被應(yīng)用到下一個(gè)興趣點(diǎn)推薦中，例如基于潛在因子模型的方法、基于馬爾可夫鏈模型的方法、嵌入表示模型的方法、以及神經(jīng)網(wǎng)絡(luò)模型的方法等。本文主要研究下一個(gè)興趣點(diǎn)推薦問題。下一個(gè)興趣點(diǎn)與傳統(tǒng)的一般興趣點(diǎn)推薦不同，在傳統(tǒng)的興趣點(diǎn)推薦中，類似于一般的商品（圖書，電影，音樂）推薦等，通常使用協(xié)同過濾的思想給用戶推薦用戶在未來將要訪問的商品，基于矩陣分解的算法是協(xié)同過濾算法最先進(jìn)的算法。該算法是基于相似的用戶通常有相似的品味的思想進(jìn)行興趣點(diǎn)推薦，它首先用隨機(jī)初始的向量表示用戶和項(xiàng)目的潛在因子，通過對(duì)用戶歷史訪問興趣得到用戶和興趣點(diǎn)的潛在因子表示，在推薦過程中，根據(jù)用戶和興趣點(diǎn)的潛在因子的內(nèi)積得到用戶未來訪問興趣點(diǎn)的概率，最終得到用戶TOP-K個(gè)推薦列表。在一般的興趣點(diǎn)推薦中，它只是根據(jù)用戶的訪問偏好推薦用戶未來訪問興趣點(diǎn)的可能性（比如未來的一天內(nèi)或者一年），嚴(yán)重忽略了用戶歷史簽到興趣點(diǎn)之間的序列關(guān)系。比如用戶按序列順序訪問A，B，C三個(gè)景點(diǎn)，在一般的興趣點(diǎn)推薦中，推薦的三個(gè)景點(diǎn)是由用戶的偏好得到，它們之間沒有任何順序關(guān)系。然而在下一個(gè)興趣點(diǎn)推薦中，用戶的序列訪問行為通常對(duì)于下一個(gè)興趣點(diǎn)也具有很重要的影響，因此在下一個(gè)興趣點(diǎn)中用戶的推薦列表是隨著簽到信息時(shí)刻變化，且用戶的每次移動(dòng)都會(huì)導(dǎo)致推薦列表劇烈的變化。許多的研究通常會(huì)把下一個(gè)興趣點(diǎn)推薦問題看成是序列推薦問題ADDINCSL_CITATION{"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.ins.2019.12.006","author":[{"dropping-particle":"","family":"Zhang","given":"Lu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sun","given":"Zhu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhang","given":"Jie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kloeden","given":"Horst","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Klanner","given":"Felix","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2020"]]},"page":"169-190","publisher":"ElsevierInc.","title":"ModelinghierarchicalcategorytransitionfornextPOIrecommendationwithuncertaincheck-ins","type":"article-journal","volume":"515"},"uris":["/documents/?uuid=abbd42db-8316-4608-aebb-353efa9d40ef"]}],"mendeley":{"formattedCitation":"^[27]","plainTextFormattedCitation":"[27]","previouslyFormattedCitation":"^[27]"},"properties":{"noteIndex":0},"schema":"/citation-style-language/schema/raw/master/csl-citation.json"}[27]。文獻(xiàn)ADDINCSL_CITATION{"citationItems":[{"id":"ITEM-1","itemData":{"abstract":"Venuerecommendationaimstoassistusersbymakingpersonalisedsuggestionsofvenuestovisit,buildingupondataavailablefromlocation-basedsocialnetworks(LBSNs)suchasFoursquare.Aparticularchallengeforthistaskiscontext-awarevenuerecommendation(CAVR),whichadditionallytakesthesurroundingcontextoftheuser(e.g.theuser'slocationandthetimeofday)intoaccountinordertoprovidemorerelevantvenuesuggestions.ToaddressthechallengesofCAVR,wedescribetwoapproachesthatexploitwordembeddingtechniquestoinferthevector-spacerepresentationsofvenues,users'existingpreferences,andusers'contextualpreferences.OurevaluationuponthetestcollectionoftheTREC2015ContextualSuggestiontrackdemonstratesthatwecansignificantlyenhancetheeffectivenessofastate-of-the-artvenuerecommendationapproach,aswellasproducecontext-awarerecommendationsthatareatleastaseffectiveasthetopTREC2015systems.","author":[{"dropping-particle":"","family":"Manotumruksa","given":"Jarana","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Macdonald","given":"Craig","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ounis","given":"Iadh","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2016"]]},"page":"2-5","title":"ModellingUserPreferencesusingWordEmbeddingsforContext-AwareVenueRecommendation","type":"article-journal"},"uris":["/documents/?uuid=14873e01-13ec-48c2-aaf1-5253b6844c0b"]}],"mendeley":{"formattedCitation":"^[32]","plainTextFormattedCitation":"[32]","previouslyFormattedCitation":"^[32]"},"properties":{"noteIndex":0},"schema":"/citation-style-language/schema/raw/master/csl-citation.json"}[14]提出了一種用于POI推薦的基于排名的地理因子分解方法，稱為RankGeoFM。該模型考慮到簽到頻率表征了用戶的訪問偏好，并通過對(duì)興趣點(diǎn)的正確排序來學(xué)習(xí)因子分解。該模型可以很容易地將不同類型的上下文信息，如地理影響和時(shí)間影響納入其中。由于用戶-興趣點(diǎn)矩陣的稀疏性帶來了嚴(yán)峻的挑戰(zhàn)。為了應(yīng)對(duì)這一挑戰(zhàn)，文獻(xiàn)ADDINCSL_CITATION{"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1145/2623330.2623638","ISBN":"9781450329569","abstract":"Point-of-Interest(POI)recommendationhasbecomeanimportantmeanstohelppeoplediscoverattractivelocations.However,extremesparsityofuser-POImatricescreatesaseverechallenge.Tocopewiththischallenge,viewingmobilityrecordsonlocation-basedsocialnetworks(LBSNs)asimplicitfeedbackforPOIrecommendation,wefirstproposetoexploitweightedmatrixfactorizationforthistasksinceitusuallyservescollaborativefilteringwithimplicitfeedbackbetter.Besides,researchershaverecentlydiscoveredaspatialclusteringphenomenoninhumanmobilitybehaviorontheLBSNs,i.e.,individualvisitinglocationstendtoclustertogether,andalsodemonstrateditseffectivenessinPOIrecommendation,thusweincorporateitintothefactorizationmodel.Particularly,weaugmentusers'andPOIs'latentfactorsinthefactorizationmodelwithactivityareavectorsofusersandinfluenceareavectorsofPOIs,respectively.Basedonsuchanaugmentedmodel,wenotonlycapturethespatialclusteringphenomenonintermsoftwo-dimensionalkerneldensityestimation,butwealsoexplainwhytheintroductionofsuchaphenomenonintomatrixfactorizationhelpstodealwiththechallengefrommatrixsparsity.Wethenevaluatetheproposedalgorithmonalarge-scaleLBSNdataset.Theresultsindicatethatweightedmatrixfactorizationissuperiortootherformsoffactorizationmodelsandthatincorporatingthespatialclusteringphenomenonintomatrixfactorizationimprovesrecommendationperformance.?2014ACM.","author":[{"dropping-particle":"","family":"Lian","given":"Defu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zhao","given":"Cong","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xie","given":"Xing","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sun","given":"Guangzhong","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chen","given":"Enhong","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rui","given":"Yong","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"ProceedingsoftheACMSIGKDDInternationalConferenceonKnowledgeDiscoveryandDataMining","id":"ITEM-1","issued":{"date-parts":[["2014"]]},"page":"831-840","title":"GeoMF:Jointgeographicalmodelingandmatrixfactorizationforpoint-of-interestrecommendation","type":"paper-conference"},"uris":["/documents/?uuid=3e562ae9-ac7e-40a4-91d1-b8b99cae7122"]}],"mendeley":{"formattedCitation":"^[30]","plainTextFormattedCitation":"[30]","previouslyFormattedCitation":"^[30]"},"properties":{"noteIndex":0},"schema":"/citation-style-language/schema/raw/master/csl-citation.json"}[15]首次提出利用加權(quán)矩陣分解來解決這一問題，因?yàn)樗ǔＤ芨玫胤?wù)于隱式反饋的協(xié)同過濾。文獻(xiàn)ADDINCSL_CITATION{"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1145/1772690.1772773","ISBN":"9781605587998","abstract":"Recommendersystemsareanimportantcomponentofmanywebsites.Twoofthemostpopularapproachesarebasedonmatrixfactorization(MF)andMarkovchains(MC).MFmethodslearnthegeneraltasteofauserbyfactorizingthematrixoverobserveduser-itempreferences.Ontheotherhand,MCmethodsmodelsequentialbehaviorbylearningatransitiongraphoveritemsthatisusedtopredictthenextactionbasedontherecentactionsofauser.Inthispaper,wepresentamethodbringingbothapproachestogether.OurmethodisbasedonpersonalizedtransitiongraphsoverunderlyingMarkovchains.Thatmeansforeachuseranowntransitionmatrixislearned-thusintotalthemethodusesatransitioncube.Astheobservationsforestimatingthetransitionsareusuallyverylimited,ourmethodfactorizesthetransitioncubewithapairwiseinteractionmodelwhichisaspecialcaseoftheTuckerDecomposition.WeshowthatourfactorizedpersonalizedMC(FPMC)modelsubsumesbothacommonMarkovchainandthenormalmatrixfactorizationmodel.Forlearningthemodelparameters,weintroduceanadaptionoftheBayesianPersonalizedRanking(BPR)frameworkforsequentialbasketdata.Empirically,weshowthatourFPMCmodeloutperformsboththecommonmatrixfactorizationandtheunpersonalizedMCmodelbothlearnedwithandwithoutfactorization.?2010InternationalWorldWideWebConferenceCommittee(IW3C2).","author":[{"dropping-particle":"","family":"Rendle","given":"Steffen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Freudenthaler","given":"Christoph","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schmidt-Thieme","given":"Lars","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Proceedingsofthe19thInternationalConferenceonWorldWideWeb,WWW'10","id":"ITEM-1","issued":{"date-parts":[["2010"]]},"note":"FromDuplicate1(FactorizingpersonalizedMarkovchainsfornext-basketrecommendation-Rendle,Steffen;Freudenthaler,Christoph;Schmidt-Thieme,Lars)\n\n2012年的FPMC不加地理位置\n\nFromDuplicate2(FactorizingpersonalizedMarkovchainsfornext-basketrecommendation-Rendle,Steffen;Freudenthaler,Christoph;Schmidt-Thieme,Lars)\n\nFPMC模型沒有地理位置限制\n\nFPMC是基于馬爾科夫鏈的模型。通過markov鏈對(duì)用戶的簽到信息建模。使用個(gè)性markov鏈和矩陣分解算法整合序列信息和用戶的一般偏好進(jìn)行next-basket推薦。","page":"811-820","title":"FactorizingpersonalizedMarkovchainsfornext-basketrecommendation","type":"article-journal"},"uris":["/documents/?uuid=12f1a349-c88e-43b9-b6da-9adb731d1013"]}],"mendeley":{"formattedCitation":"^[39]","plainTextFormattedCitation":"[39]","previouslyFormattedCitation":"^[39]"},"properties":{"noteIndex":0},"schema":"/citation-style-language/schema/raw/master/csl-citation.json"}[16]考慮到矩陣分解的方法難于對(duì)興趣點(diǎn)序列信息建模，而基于馬爾科夫鏈的方法能夠捕獲用戶的簽到序列信息，因此文獻(xiàn)ADDINCSL_CITATION{"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1145/1772690.1772773","ISBN":"9781605587998","abstract":"Recommendersystemsareanimportantcomponentofmanywebsites.Twoofthemostpopularapproachesarebasedonmatrixfactorization(MF)andMarkovchains(MC).MFmethodslearnthegeneraltasteofauserbyfactorizingthematrixoverobserveduser-itempreferences.Ontheotherhand,MCmethodsmodelsequentialbehaviorbylearningatransitiongraphoveritemsthatisusedtopredictthenextactionbasedontherecentactionsofauser.Inthispaper,wepresentamethodbringingbothapproachestogether.OurmethodisbasedonpersonalizedtransitiongraphsoverunderlyingMarkovchains.Thatmeansforeachuseranowntransitionmatrixislearned-thusintotalthemethodusesatransitioncube.Astheobservationsforestimatingthetransitionsareusuallyverylimited,ourmethodfactorizesthetransitioncubewithapairwiseinteractionmodelwhichisaspecialcaseoftheTuckerDecomposition.WeshowthatourfactorizedpersonalizedMC(FPMC)modelsubsumesbothacommonMarkovchainandthenormalmatrixfactorizationmodel.Forlearningthemodelparameters,weintroduceanadaptionoftheBayesianPersonalizedRanking(BPR)frameworkforsequentialbasketdata.Empirically,weshowthatourFPMCmodeloutperformsboththecommonmatrixfactorizationandtheunpersonalizedMCmodelbothlearnedwithandwithoutfactorization.?2010InternationalWorldWideWebConferenceCommittee(IW3C2).","author":[{"dropping-particle":"","family":"Rendle","given":"Steffen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Freudenthaler","given":"Christoph","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Schmidt-Thieme","given":"Lars","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Proceedingsofthe19thInternationalConferenceonWorldWideWeb,WWW'10","id":"ITEM-1","issued":{"date-parts":[["2010"]]},"note":"FromDuplicate1(FactorizingpersonalizedMarkovchainsfornext-basketrecommendation-Rendle,Steffen;Freudenthaler,Christoph;Schmidt-Thieme,Lars)\n\n2012年的FPMC不加地理位置\n\nFromDuplicate2(FactorizingpersonalizedMarkovchainsfornext-basketrecommendation-Rendle,Steffen;Freudenthaler,Christoph;Schmidt-Thieme,Lars)\n\nFPMC模型沒有地理位置限制\n\nFPMC是基于馬爾科夫鏈的模型。通過markov鏈對(duì)用戶的簽到信息建模。使用個(gè)性markov鏈和矩陣分解算法整合序列信息和用戶的一般偏好進(jìn)行next-basket推薦。","page":"811-820","title":"FactorizingpersonalizedMarkovchainsfornext-basketrecommendation","type":"article-journal"},"uris":["/documents/?uuid=12f1a349-c88e-43b9-b6da-9adb731d1013"]}],"mendeley":{"formattedCitation":"^[39]","plainTextFormattedCitation":"[39]","previouslyFormattedCitation":"^[39]"},"properties":{"noteIndex":0},"schema":"/citation-style-language/schema/raw/master/csl-citation.json"}[16]引進(jìn)了個(gè)性化的馬爾科夫鏈，通過結(jié)合矩陣分解和一階馬爾科夫兩者的優(yōu)勢(shì)分別對(duì)用戶的偏好和訪問的商品的序列信息進(jìn)行建模，提出個(gè)性化的馬爾科夫鏈分解的算法（FPMC）對(duì)用戶購(gòu)買商品進(jìn)行建模，用于給用戶推薦用戶下一個(gè)購(gòu)買的商品，同時(shí)通過在數(shù)據(jù)集上的實(shí)驗(yàn)結(jié)果也顯示出FPMC的方法要優(yōu)于一般的矩陣分解算法和單純的基于馬爾科夫模型的算法。與下一個(gè)商品推薦不同，由于興趣點(diǎn)推薦中不僅受到序列信息和用戶偏好的影響，用戶的地理位置信息對(duì)于下一個(gè)興趣點(diǎn)推薦也有一定程度的影響，文獻(xiàn)ADDINCSL_CITATION{"citationItems":[{"id":"ITEM-1","itemData":{"ISBN":"9781577357384","ISSN":"10450823","abstract":"TherapidlygrowingofLocation-basedSocialNetworks(LBSNs)providesavastamountofcheck-indata,whichenablesmanyservices,e.g.,point-of-interest(POI)recommendation.Inthispaper,westudythenextnewPOIrecommendationprobleminwhichnewPOIswithrespecttousers'currentlocationaretoberecommended.Thechallengeliesinthedifficultyinpreciselylearningusers'sequentialinformationandpersonalizingtherecommendationmodel.Tothisend,weresorttotheMetricEmbeddingmethodfortherecommendation,whichavoidsdrawbacksoftheMatrixFactorizationtechnique.Weproposeapersonalizedrankingmetricembeddingmethod(PRME)tomodelpersonalizedcheck-insequences.WefurtherdevelopaPRME-Gmodel,whichintegratessequentialinformation,individualpreference,andgeographicalinfluence,toimprovetherecommendationperformance.Experimentsontworeal-worldLBSNdatasetsdemonstratethatournewalgorithmoutperformsthestate-of-the-artnextPOIrecommendationmethods.","author":[{"dropping-particle":"","family":"Feng","given":"Shanshan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Xutao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zeng","given":"Yifeng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cong","given":"Gao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chee","given":"YeowMeng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yuan","given":"Quan","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"IJCAIInternationalJointConferenceonArtificialIntelligence","id":"ITEM-1","issued":{"date-parts":[["2015"]]},"note":"PRME-G\n本文唯一的創(chuàng)新，通過嵌入的方法對(duì)興趣點(diǎn)序列偏好和用戶偏好建模。\n本文使用了2個(gè)嵌入向量空間。序列變換嵌入向量空間，用戶和興趣點(diǎn)向量嵌入空間。分別對(duì)用戶偏好和序列偏好建模。\n針對(duì)FPMC的創(chuàng)新有一點(diǎn)。比如A-B，B-C。由于MC方法的獨(dú)立性，A到C的潛在轉(zhuǎn)移可能性被忽略。\n但是ME使用嵌入方法，A，B，C用嵌入空間表示，A，C的轉(zhuǎn)移可能性也可以被捕獲。\n因此ME的方法由于MF的方法。","page":"2069-2075","title":"PersonalizedrankingmetricembeddingfornextnewPOIrecommendation","type":"paper-conference","volume":"2015-Janua"},"uris":["/documents/?uuid=a3d32887-ce52-489b-b7ff-eb79ffda9c4b"]}],"mendeley":{"formattedCitation":"^[42]","plainTextFormattedCitation":"[42]","previouslyFormattedCitation":"^[42]"},"properties":{"noteIndex":0},"schema":"/citation-style-language/schema/raw/master/csl-citation.json"}[17]提出了PRME模型，該模型同時(shí)考慮了用戶偏好和序列信息的影響，并利用度量嵌入的方法對(duì)用戶的簽到數(shù)據(jù)信息進(jìn)行建模。它使用兩個(gè)嵌入空間，用戶偏好嵌入空間和序列信息嵌入空間。在用戶偏好嵌入空間中用戶與興趣點(diǎn)在嵌入空間的距離表示用戶對(duì)興趣點(diǎn)的偏好。在序列嵌入空間中，興趣點(diǎn)之間的的嵌入距離表示興趣點(diǎn)之間轉(zhuǎn)化的可能。然后通過權(quán)重的形式整合用戶偏好和序列信息的影響。為了整合地理位置影響，文獻(xiàn)ADDINCSL_CITATION{"citationItems":[{"id":"ITEM-1","itemData":{"ISBN":"9781577357384","ISSN":"10450823","abstract":"TherapidlygrowingofLocation-basedSocialNetworks(LBSNs)providesavastamountofcheck-indata,whichenablesmanyservices,e.g.,point-of-interest(POI)recommendation.Inthispaper,westudythenextnewPOIrecommendationprobleminwhichnewPOIswithrespecttousers'currentlocationaretoberecommended.Thechallengeliesinthedifficultyinpreciselylearningusers'sequentialinformationandpersonalizingtherecommendationmodel.Tothisend,weresorttotheMetricEmbeddingmethodfortherecommendation,whichavoidsdrawbacksoftheMatrixFactorizationtechnique.Weproposeapersonalizedrankingmetricembeddingmethod(PRME)tomodelpersonalizedcheck-insequences.WefurtherdevelopaPRME-Gmodel,whichintegratessequentialinformation,individualpreference,andgeographicalinfluence,toimprovetherecommendationperformance.Experimentsontworeal-worldLBSNdatasetsdemonstratethatournewalgorithmoutperformsthestate-of-the-artnextPOIrecommendationmethods.","author":[{"dropping-particle":"","family":"Feng","given":"Shanshan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Li","given":"Xutao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zeng","given":"Yifeng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cong","given":"Gao","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chee","given":"YeowMeng","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yuan","given":"Quan","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"IJCAIInternationalJointConferenceonArtificialIntelligence","id":"ITEM-1","issued":{"date-parts":[["2015"]]},"note":"PRME-G\n本文唯一的創(chuàng)新，通過嵌入的方法對(duì)興趣點(diǎn)序列偏好和用戶偏好建模。\n本文使用了2個(gè)嵌入向量空間。序列變換嵌入向量空間，用戶和興趣點(diǎn)向量嵌入空間。分別對(duì)用戶偏好和序列偏好建模。\n針對(duì)FPMC的創(chuàng)新有一點(diǎn)。比如A-B，B-C。由于MC方法的獨(dú)立性，A到C的潛在轉(zhuǎn)移可能性被忽略。\n但是ME使用嵌入方法，A，B，C用嵌入空間表示，A，C的轉(zhuǎn)移可能性也可以被捕獲。\n因此ME的方法由于MF的方法。","page":"2069-2075","title":"PersonalizedrankingmetricembeddingfornextnewPOIrecommendation","type":"paper-conference","volume":"2015-Janua"},"uris":["/documents/?uuid=a3d32887-ce52-489b-b7ff-eb79ffda9c4b"]}],"mendeley":{"formattedCitation":"^[42]","plainTextFormattedCitation":"[42]","previouslyFormattedCitation":"^[42]"},"properties":{"noteIndex":0},"schema":"/citation-style-language/schema/raw/master/csl-citation.json"}[17]進(jìn)一步通過地理位置作為權(quán)重信息通過指數(shù)衰減的形式控制用戶訪問下一個(gè)興趣點(diǎn)的不同偏好。文獻(xiàn)ADDINCSL_CITATION{"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1145/2983323.2983711","ISBN":"9781450340731","abstract":"Withtherapidprevalenceofsmartmobiledevicesandthedramaticproliferationoflocation-basedsocialnetworks(LB-SNs),location-basedrecommendationhasbecomeanimportantmeanstohelppeoplediscoverattractiveandinterestingpointsofinterest(POIs).However,extremesparsityofuser-POImatrixandcold-startissuecreateseverechallenges,causingCF-basedmethodstodegradesignificantlyintheirrecommendationperformance.Moreover,location-basedrecommendationrequiresspatiotemporalcontextawarenessanddynamictrackingoftheuser'slatestpreferencesinareal-timemanner.Toaddressthesechallenges,westandonrecentadvancesinembeddinglearningtechniquesandproposeagenericgraph-basedembeddingmodel,calledGE,inthispaper.GEjointlycapt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ofuseractivity,butalsoitssemanticinformation.LBSNscanhelptounderstandmobileusers'spatialtemporalactivitypreference(STAP),whichcanenableawiderangeofubiquitousapplications,suchaspersonalizedcontext-awarelocationrecommendationandgroup-orientedadvertisement.However,modelingsuchuser-specificSTAPneedstotacklehigh-dimensionaldata,i.e.,user-location-time-activityquadruples,whichiscomplicatedandusuallysuffersfromadatasparsityproblem.Inordertoaddressthisproblem,weproposeaSTAPmodel.Itfirstmodelsthespatialandtemporalactivitypreferenceseparately,andthenusesaprinciplewaytocombinethemforpreferenceinference.Inordertocharacterizetheimpactofspatialfeaturesonuseractivitypreference,weproposethenotionofpersonalfunctionalregionandrelatedparameterstomodelandinferuserspatialactivitypreference.InordertomodeltheusertemporalactivitypreferencewithsparseuseractivitydatainLBSNs,weproposetoexploitthetemporalactivitysimilarityamongdifferentusersandapplynonnegativetensorfactorizationtocollaborativelyinfertemporalactivitypreference.Finally,weputforwardacontextawarefusionframeworktocombinethespatialandtemporalactivitypreferencemodelsforpreferenceinference.Weevaluateourproposedapproachonthreereal-worlddatasetscollectedfromNewYorkandTokyo,andshowthatourSTAPmodelconsistentlyoutperformsthebaselineapproachesinvarioussettings.","author":[{"dropping-particle":"","family":"Yang","given":"Dingqi","non-dropping-parti