Data Science Salon: Applications of Embeddings and Deep Learning at Groupon

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Bojan Babick, Senior Software Engineer at Groupon talks about how the Groupon technical team went on a journey to switch from rule-based systems to classical machine learning models with hand-designed features to representation learning and deep learning See the related post here: https://roundtable.datascience.salon/applications-of-embeddings-and-deep-learning-at-groupon Sign up for DSSInsider to see the full video: https://insider.datascience.salon/ Next DSS SEA Event - https://datascience.salon/seattle/

Datos y análisis

Deep Learning @ Groupon
Applications within Relevance and Ranking
Data Science Salon Austin, 21-22th February 2019

The Presenter
Bojan Babic is a Senior Engineer at Groupon working on core
search and relevance in both personalized deals search and
deal recommendations
@bojanbabic

Who are we?
Groupon - Dynamic Marketplace

Four Distinct Customer Journeys
Search Browse Home Feed Similar Deals to Consider
Examples of use of DL @ Groupon

Search
Using Query Understanding to impact the recall of the long tail queries

Query Similarity
● TF-IDF - bag of words approach
○ Never consider queries unless they share the same terms
○ Examples: “nail clippers” vs “la clippers”
● Random Walk in bipartite graph of queries and categories
○ no guarantee that similar queries have same search results
● Doc2Vec - get k-closest queries in the embeddings space (PV-DM)
○ Improved recall of the tail queries, better overall precision
○ Examples: “sony playstation” -> “playstation 4”, “ps4”, “psp”

Browse
Leveraging deal classification to guide the customer through Groupon’s vast catalog

Learned Taxonomy
Hyperparameters
● batch size: 64
● epochs: 30
● sequence length: 200 words
● dropout: 0.2
What we tried:
● K-NN on the vector dense representation of the deal description
● Traditional (shallow) ML (GBM, SVM, logistic regression, etc.)
What worked:
● CNN
● LSTM

Home Feed
Understanding how images influence purchases in recommended feed

Image Propensity to Purchase
Which one would you take a bite off
It is well known that a picture is worth a thousand words and, at Groupon, images play a fundamental role in the marketing of deals.

Similar Deals to Consider
Recommending Similar deals by Leveraging User Session Information

Recommending Similar Deals
Learning from Consumers Journeys
Deal2Vec

Conclusions
● All-in in replacing traditional feature engineering with respective embeddings representation
● Expanding Deep Learning reach within the Groupon to other areas (ie mobile - credit card detection)
● More work in automating feature discovery and model parameter tuning

References
1. Comparative Study of CNN and RNN for Natural Language Processing, Wenpeng Yin, Katharina
Kann, Mo Yu and Hinrich Schutze, IBM 2017
2. Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising, Yahoo! 2016
3. The Evolution of a Real-World Recommender System, Pinterest 2016
4. Deep Neural Networks for YouTube Recommendations, ACM 2016
5. Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for
Recommender Systems. Computer 42, 8 (August 2009), 30-37.
6. Yann LeCun, Patrick Haffner, Léon Bottou, and Yoshua Bengio. 1999. Object Recognition with
Gradient-Based Learning. In Shape, Contour and Grouping in Computer Vision, David A.
Forsyth, Joseph L. Mundy, Vito Di Gesù, and Roberto Cipolla (Eds.). Springer-Verlag, London, UK,
UK, 319-.

We need your help!
relevance-jobs@groupon.com

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1. Deep Learning @ Groupon Applications within Relevance and Ranking Data Science Salon Austin, 21-22th February 2019

2. The Presenter Bojan Babic is a Senior Engineer at Groupon working on core search and relevance in both personalized deals search and deal recommendations @bojanbabic

3. Who are we? Groupon - Dynamic Marketplace

4. Building Daily Habit

5. Four Distinct Customer Journeys Search Browse Home Feed Similar Deals to Consider Examples of use of DL @ Groupon

6. Search Using Query Understanding to impact the recall of the long tail queries

7. Query Similarity ● TF-IDF - bag of words approach ○ Never consider queries unless they share the same terms ○ Examples: “nail clippers” vs “la clippers” ● Random Walk in bipartite graph of queries and categories ○ no guarantee that similar queries have same search results ● Doc2Vec - get k-closest queries in the embeddings space (PV-DM) ○ Improved recall of the tail queries, better overall precision ○ Examples: “sony playstation” -> “playstation 4”, “ps4”, “psp”

8. Model Validation

9. Browse Leveraging deal classification to guide the customer through Groupon’s vast catalog

10. Complex deal taxonomy

11. Learned Taxonomy Hyperparameters ● batch size: 64 ● epochs: 30 ● sequence length: 200 words ● dropout: 0.2 What we tried: ● K-NN on the vector dense representation of the deal description ● Traditional (shallow) ML (GBM, SVM, logistic regression, etc.) What worked: ● CNN ● LSTM

12. Home Feed Understanding how images influence purchases in recommended feed

13. Image Propensity to Purchase Which one would you take a bite off It is well known that a picture is worth a thousand words and, at Groupon, images play a fundamental role in the marketing of deals.

14. Similar Deals to Consider Recommending Similar deals by Leveraging User Session Information

15. Recommending Similar Deals Learning from Consumers Journeys Deal2Vec

16. Investment on Embeddings

17. Conclusions ● All-in in replacing traditional feature engineering with respective embeddings representation ● Expanding Deep Learning reach within the Groupon to other areas (ie mobile - credit card detection) ● More work in automating feature discovery and model parameter tuning

18. References 1. Comparative Study of CNN and RNN for Natural Language Processing, Wenpeng Yin, Katharina Kann, Mo Yu and Hinrich Schutze, IBM 2017 2. Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising, Yahoo! 2016 3. The Evolution of a Real-World Recommender System, Pinterest 2016 4. Deep Neural Networks for YouTube Recommendations, ACM 2016 5. Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. Computer 42, 8 (August 2009), 30-37. 6. Yann LeCun, Patrick Haffner, Léon Bottou, and Yoshua Bengio. 1999. Object Recognition with Gradient-Based Learning. In Shape, Contour and Grouping in Computer Vision, David A. Forsyth, Joseph L. Mundy, Vito Di Gesù, and Roberto Cipolla (Eds.). Springer-Verlag, London, UK, UK, 319-.

19. We need your help! relevance-jobs@groupon.com

20. Q & A

Data Science Salon: Applications of Embeddings and Deep Learning at Groupon

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Data Science Salon: Applications of Embeddings and Deep Learning at Groupon