Transferring Deep Learning into a. Recommender System

Transcription

1 Transferring Deep Learning into a Big Data Paris 2017 Recommender System By Christophe Duong, Data Scientist

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3 Mostly flash sales, i.e. volatile, ephemeral -> Unlike amazon / Price Minister / Cdiscount -> Sales are seasonal (Christmas, ski, summer) High-end Product -> Appearance is crucial Fewer recurrent buyers -> Web visit patterns are essential -> Short visit sessions (browsing 4-10 first sales) Challenging context for standard recommender systems

4 A data science workflow with Six steps to a predictive model Business Understandin g Scored dataset Data Preparation Model Creation Deployment Scored dataset Dataset 1 Iteration 1 Iteration 2 Iteration n Dataset 2 Data Acquisition Data Exploration & Understandin g Evaluation Creating a predictive model is a highly iterative process. Data Science Studio enables its users to create and manage these projects from end-to-end. Dataset n Adapted from the CRISP-DM methodology This process is not industry specific, and can be applied to many use cases.

5 Basic Recommendation Engines

6 Other Factors

7 One Meta Model to Rule Them All Describe Recommend Recommenders as features Combine Machine learning to optimize purchasing probability

8 Recommender system for Home Page Ordering +7% revenue (A/B testing) Customer visits Purchases Cleaning, combining and enrichment of data Recommendation Engines Meta Model Optimization of home display Sales information the application automatically runs and compiles heterogeneous data Generation of recommendations based on user behaviour Meta model combine recommendations to directly optimize purchasing probability Every customer is shown the 10 sales he is the most likely to buy Sales Images Batch Scoring every night

9 Integrating Image Information CONTENT BASED Sea + Beach +Forest + Hotel Pool + Palm Trees Hotel + Mountains Sales Images Labelling Model Pool + Forest + Hotel + Sea Sales descriptions Recommender System

10 Using Deep Learning models Common Issues I don t have GPUs server I don t have a deep leaning expert I don t have labelled data (or too few) I don t have the time to wait for model training I don t want to pay for private apis / I m afraid their labelling will change over time

11 Solution 1 : Pre trained models I don t have (or few) labelled data -> Is there similar data? VPG PLACES DATABASE SUN DATABASE 205 categories 2.5 M images 307 categories 110 K images

12 Solution 1 : Pre trained models If there is open data, there is an open pre trained model! Kudos to the community Check the licensing Example with Places (Caffe Model Zoo) : swimming_pool/outdoor: 0.65 inn/outdoor: 0.06 tower: 0.53 skyscraper: 0.26

13 Solution 2 : Transfer Learning I want to add information of SUN database But I have only 100 K images If you know how to recognize after a little bit of training you will be able to recognize Transfer Learning Use a network that knows how to see As a feature generator / transformer To be updated for the new problem

14 Solution 2 : Transfer Learning Leverage existing knowledge! PLACES DATABASE SUN DATABASE VOYAGE PRIVE Training (optional) Caffe Model Zoo CPU Pre-trained model VGG16 GPU Transferred Data : Last convolutional layer features GPU Re-Training Re-trained model TensorFlow 2 fully connected layers tower: 0.53 skyscraper: 0.26 Accuracy: 72%, Top-5 Acc: 90 % > state of the art on dataset alone

15 Resulting Labels Issue with our approach: Complementary information Redondant information Solution : Matrix Factorization = 200x200 pixels -> 600 tags => 30 themes

16 Image content detection Topic scores determine the importance of topics in an image TOPIC TOPIC SCORE (%) Golf course Fairway Putting green 31 Hotel Inn Apartment building outdoor 30 Swimming pool Lido Deck Hot tub outdoor 22 Beach Coast - Harbor 17 TOPIC TOPIC SCORE (%) Tower Skyscraper Office building 62 Bridge River Viaduct 38

17 Results? All Visits : Mostly France Pool displayed First Recommendation Fail to display pools Only Images? Pool all around the world Third column = Mix

18 What s Next? Kenya Pragu e Berlin Cambodia

19 Conclusion Deep Learning? Is there existing data? Is there a pre-trained model? Transfer Learning Cheaper, faster Any Data Scientist can do it Do Agile Data Science! Start simple Validate each steps Iterate and grow