Deep Learning Application within search and ranking at Groupon

Editor's Notes

1. Collection of techniques that we have applied in large scale and sophisticated recommendation system Showcase what we have tried, lessons learned and evolution of the thinking
2. We serving inventory of over 1m merchants to 50 mil customers. Business organized in verticals: local, goods, tickets, getaways just to name few Pumped $20b into local business
3. The group we work is working on building daily habit.
4. Search uses Query Understanding Browse uses Deal Classification Home Feed uses Image Image Processing Post-Purchase uses Deal Similarity
5. Vanila examples do not work in production Steep learning curve from basic examples to real production example in ranking Steep learning curve from vanila examples to real production applications convolutions work well on 1D vectors (text) as well as on 2D vector representation (images). Instead of edges, curves and diagonals, 1D convolutions detect n-grams. Infact Conv nets can be used on any data with spacial patterns feature engineering can get you only far to certain point. as much we covered first level features and used age, gender, location or calculates features like propensity to category or to travel. there are latent features that we can’t simply do not have access to. there is when embeddings kick in. still getting from word2vec to sequence model application in recommendation systems is not trivial. this talk has task of bridging that gap and showcasing set of applications we used that cover 4 main areas that recommender system at groupon care about: search, browse, homefeed and post purchase.
6. Make sure that performance of the model does not deteriorate with subsequence model release. Need to have robust validation. Intrinsic validation requires curated list of analogies
7. 1-D Convolutions on text vs 2-D Convolutions on the images
8. Taxonomies are super important for recall. Taxonomies have 1500 nodes. Complexity of having multiple taxonomies New partnerships we have require incorporating new orthogonal taxonomies (ie food delivery restaurants, place has wifi) Problem of bad taxonomy mapping can have big impact on the business. We do need safety measures in order to suppress human errors. Human errors: missed opportunity and/or misclassified deal.
9. Corpus considered title, short description, highlight and fineprint Why not CNNs? Accuracy of the models vary on the length of the input sequence - CNNs focus on small regions in order to extract important classification features, while LSTMs consider the whole input sequence Embedding is calculated by taking the output of the LSTM cell at last time step and multiplying it with another weight matrix, normalizing it and put through softmax classifier that had 1500 classes. Trying whole range of the hyperparameters, but we settled with batch size 64 and sequence length 200 and dropout 0.2 Scoring time use target (category) embedding in order to find cosine similarity with vector representation of the input
10. We use a Convolutional Neural Network (CNN) to train a model using deal views <image, target> to predict an image’s propensity to purchase (IPP) The target class is a binary purchase/no-purchase label based on the customer’s decision made after viewing the deal We use the precomputed IPP as a feature in our proprietary learning-to-rank algorithm used for ranking deals in the Home Feed