Download - Final Report Jarvis - Open Food Data Program · Rendle [4] [5] introduces the factorization machines, an approach combining the power of factorization models with the generality of

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FinalReportbyJarvisThefollowingreportwascompletedbytheprojectteammembersintheendofOctober2017asafinalreportforthefirstincubationphase.Thisdoesnotmeanthattheincubationnortheprojectareatafinalstage.WhatwasyourVision?Chatbotsareanewwaytointeractwithcompanies,productsandservices.Theproductisthesame,buttheinterfacebetweentheproductandtheuserisdifferent.Accesstoinformationhaschangedintherecentyearsthankstothehugeadvancesinpredictivealgorithms.Therelevantinformationisdisplayedtotheuserevenbeforeherequestsit.Everythingisbasedonthetastesandpreferencesoftheuser.Ourprojectisthecreateapersonalizedrecommendersystemforcookingrecipesavailablethroughafriendlyconversationalinterface.Whatgoalsdidyousetfortheincubationtimeframe?

– Buildarecommendersystembasedonadatasetofrecipesanduserinteractions– Buildachatbotinterfacethatwillguidetheuserthroughtherecommendation

processWhatopendatadidyouuseorgenerate?

– weusetheSwissFoodCompositionDatabase:http://www.naehrwertdaten.ch– we’llgeneratedataaboutpeople’sfoodconsumption

Whatdidyouaccomplish?

– Scrapalltherecipesfromtheallrecipeswebsite(45krecipes,~4.5Minteractionswith~1Musers)

– BuildachatbotprototypeofJarviswithDialogflow(ourfirstideas,butthefinalgoalchangedsincethen)

– GatherinformationaboutchatbotdesignandrecommendersystemsHowdidOpenDatasupportyou?

– Withfinancing– AmeetingwithHannesGassert(29.07.2017):brainstorming,strategies,ideation– AmeetingwithThomasRippel(10.08.2017):brainstorming,knowledgesharing

FeedbackforOpenData

– ThestrategicmeetingwithHanneswasreallyinterestingandhelpfulinthewaythatheknowswhathesaysandhasalotofexperienceinthedomain.

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– Thewayyouadapttoeachteamisreallygood(eg.wedonotneedofficessothemoneyisusedforsomethingmoreusefulforthesuccessoftheproject,andsoon)

Whatarethenextsteps?

– Design,developmentandvalidationofthereciperecommendersystemmodelusingcollecteddata.~15hours*person/weekforthenext10weeks,withtheassistanceofLSIRlabatEPFL.Thecollecteddatacontainsbothimplicit(madeit/reviews)andexplicitfeedback(ratings)fromusertorecipes.

TherecommendermodelwillbebasedonBayesianPersonalizedRanking,usingimplicitfeedback,whichisexpectedtooutperformstandardlearningtechniques[1].ThecurrentdevelopmentseemstoleadtoaMatrix-factorization[2]basedmodel.

Amodelusingtheexplicitrankingsdatamightbedevelopedinparallelinordertoevaluateandcomparetheirrespectiveperformancesandvalidatethechoiceofusingimplicitdata.Otherwaysofevaluationthemodelwillbeexplored[3].

Rendle[4][5]introducesthefactorizationmachines,anapproachcombiningthepoweroffactorizationmodelswiththegeneralityofstandardfeaturesengineering.Sincethecollecteddatacontainslotsoffeaturesforrecipesitwouldbeinterestingtoimplementitinourmodelandcomparetheresults.

LatestresearchesonNeuralNetworkssuggestthattheuseofanunderlying(deep)neuralnetworksoutperformsstandardfactorizationmethodsforrecommendersystem[6].Onthelong-termitmightbeinterestingtotrysomethingandseeifitisactuallyofanyhelptoenhancetheperformanceofourmodel,butatsuchanearlystagebeginningwithafactorizationbasedmodelisbest.

– Integrateconstraintintherecommendersystem,allowingforexampleto

proposeasetofrecipesthatcomplywithspecificnutritiongoals.(Vegan,Low-meat,WHO’snutritionalguidelines)

– Adapttherecommendersystemtolegallyavailabledata(TBD).– Creationofthepersonalityofthechatbot– Designoftheconversationalflow– Integrationoftherecommenderinthechatbot.Sincewewillhaveno

informationabouttheusersandrecipesatthebeginning,wewillneedtoimplementasmartcold-startrecommendationsstrategy.[7]proposessuchastrategythatbasicallyconsistinmappingusersoritemsfeaturestothelatentfactorsofourmodel.Weneedtodesignasmartstrategytoaskusersfortheirpreference,forexamplebyproposingthemrecipes/ingredientsandletthemswipeleft/rightaccordingtotheirpreferencesanduseittobuildaprofile.Withasufficientamountofuserwecouldthenusetheprofiletomapthemtothelatentfactorsofourmodelandbeabletorecommendrecipestoanewuser.

– FindanewnameandbrandimagefortheJarvisproject– Regardingopendata,we’llbereleasingaggregatedandanonymizeddata

generatedbyourplatformcontainingusersinteractions,behavioursandevolutions.Thisdatawillbefreelyavailableonline,andwouldprovideaninterestingdatasetforscientisttryingtofindpatternsinpeople’sfoodconsumption.

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– Expectedtimeframe:January2018:– endoftherecommendersystemprototype– endofthebasicchatbotFebruary2018:– adaptingtherecommendersystemtonewrecipes(legallyavailable)– integratingtherecommendersystemtothechatbotMarch2018:– firsttestofthebotwithrealusers– creationofbranding&launchstrategy)

TeamMembers&Contact

– JackyCasas,[email protected],@jackycasas_– NathanQuinteiro,[email protected],@nathan_quint

OpenDataContactNikkiBöhler,ProjectLeaderOpenData.ch,[email protected]:[1]:Rendle,Freudenthaler,GantnerandSchmidt-Thieme-BPR:BayesianPersonalizedRankingfromImplicitFeedbackhttps://arxiv.org/pdf/1205.2618.pdf[2]:SrebroandJaakkola-WeightedLow-RankApproximationshttps://www.aaai.org/Papers/ICML/2003/ICML03-094.pdf[3]:ShaniandGunawardana-EvaluatingRecommendationSystemshttp://www.bgu.ac.il/~shanigu/Publications/EvaluationMetrics.17.pdf[4]:Rendle-FactorizationMachineshttps://pdfs.semanticscholar.org/2ef7/d506b25731d0f3ec0c8f90b718b6e5bbd069.pdf[5]:Rendle-FactorizationMachineswithlibFMhttp://www.csie.ntu.edu.tw/~b97053/paper/Factorization%20Machines%20with%20libFM.pdf[6]:Xiangnan,Lizi,Hanwang,Liqiang,XiaandTat-Seng-NeuralCollaborativeFilteringhttp://papers.www2017.com.au.s3-website-ap-southeast-2.amazonaws.com/proceedings/p173.pdf[7]:Gantner,Drumond,Freudenthaler,RendleandSchmidt-Thiem-LearningAttribute-to-FeatureMappingsforCold-StartRecommendationshttps://www.semanticscholar.org/paper/Learning-Attribute-to-Feature-Mappings-for-Cold-St-Gantner-Drumond/1535d5db7078c85f0e2d565860a0fb4053a6090c

Download - Final Report Jarvis - Open Food Data Program · Rendle [4] [5] introduces the factorization machines, an approach combining the power of factorization models with the generality of

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