t112017-数据工程和技术分会场用于图像标记的应用深度学习-旅行推荐引擎应用_h

1、Pragmatic Deep Learning for image labelling- An application to a travel recommendation engine主讲人：Dataiku数据科学家 Alexandre HubertOutlineResultsMore images !Labeling ImagesPragmatic deep learning for dummiesIntroduction and ContextPost ProcessingAKA: Image for BI on steroidsIterative building of a recom

2、mender systemDataikuData Science Software Editor of Dataiku DSSFounded in 201390 + employees, 100 + clientsParis, New-York, London, San Francisco, SingaporeDESIGNPREPAREANALYSEMODELLoad and prepareVisualize and shareBuild your your datayour workmodelsPRODUCTIO NAUTOMATEMONITORSCORERe-execute yourFol

3、low your productionGet predictions workflow at easeenvironmentin real time Key FiguresE-business vacation retailerNegotiate the best prize for their clients Discount luxury18 Millions of clients.Hundreds of sales opened everydaySale Image is paramountPurchase is impulsiveSpecificitiesHighly temporar

4、y sales- Classical recommender system fail- Time event linked (Christmas, ski, summer)Expensive Product- Few recurrent buyers- Appearance counts a lotIterative Building of a Recommender SystemBasic Recommendation EnginesOther FactorsOne Meta Model to Rule Them AllDescribeRecommendRecommenders as fea

5、turesCombineMachine learning to optimizepurchasing probabilityRecommender system for Home Page Ordering+7% revenue(A/B testing)Optimization of home displayCustomer visits PurchasesMeta ModelRecommendation EnginesCleaning, combining and enrichment of dataSales informationEvery customer is shown the 1

6、0 sales he is the most likely to buyGeneration of recommendations based on user behaviourthe application automatically runs and compiles heterogeneous dataMetal model combine recommendations to directly optimize purchasing probabilitySales ImagesBatch Scoring every nightWhy use Image ?A picture is w

7、orth a thousand wordsWe want do distinguish Ski Sun and Beach Integrating Image InformationCONTENT BASEDSea + Beach +Forest + HotelPool + Palm Trees Hotel+ MountaPool + Forest + Hotel + SeaLabeling ModelSales ImagesSales descriptions vectorRecommender SystemImage Labelling For Recommendation EngineP

8、ragmatic Deep learning for “Dummies”Using Deep Learning modelsCommon Issues“I dont have a deep leaning expert”“I dont have GPUs server”“I dont have labelled data” (or too few)“I dont have the time to wait for model training ”I dont want to pay to pay for private apis” / “Im afraid their labelling wi

9、ll change over time”Solution 1 : Pre trained models“I dont have (or few) labelled data”- Is there similar data ?USPLACES DATABASESUN DATABASE205 categories2.5 M images307 categories110 K imagesSolution 1 : Pre trained modelsIf there is open data, there is an open pre trained model !Kudos to the comm

10、unityCheck the licensingExample with Places (Caffe Model Zoo) :swimming_pool/outdoor: 0.65inn/outdoor: 0.06tower: 0.53skyscraper: 0.26Solution 2 : Transfer LearningCredit : Fei-Fei Li & Andrej Karpathy & Justin Johnson /slides/winter1516_lecture11.pdfSolution 2 : Transfer Le

11、arningLeverage existing knowledge !OUR DATAPLACES DATABASESUN DATABASECaffe ModelZooTraining(optional)CPUGPUGPUtower: 0.53skyscraper: 0.26Re-TrainingTransferred Data :Last convolutional layer featuresRe-trained modelTensorFlow2 fully connected layersPre-trained modelVGG16Accuracy: 72%, Top-5 Acc: 90

12、 % state of the art on dataset alonePost Treatment & ResultsUsing Images information for BI on steroids(Or how we transfer the labelling information)Labels post-processingIssue with our approach:Redondant informationComplementary informationSolution : NMF Matrix FactorizationDimensionReductionSparsi

13、tyBalancednessExplicabilityImage content detectionTopic scores determine the importance of topics in an imageTOPICTOPIC SCORE (%)Tower Skyscraper Office building62Bridge River Viaduct38TOPICTOPIC SCORE (%)Golf course Fairway Putting green31Hotel Inn Apartment building outdoor30Swimming pool Lido Dec

14、k Hot tub outdoor22Beach Coast - Harbor17Results ?1) Visits : France and Morocco Pool displayed2) First Recommendation Mostly France & Mediterranean Fails to display pools3) Only Images recommendation Pool all around the world Does not respect budget4) Third column = Right Mix1)2)3)4)ConclusionDo it

15、erative data science !Start simple and grow Evaluate at each stepsImage labelling = BI on steroidsDeep LearningDont start from scratch ! Is there existing data ?Is there a pre-trained model ?Transfer LearningKick-start your project Gain time and moneyAny Data Scientist can do itWhats next ?Learned al