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Recsys 2015: Making Meaningful Restaurant Recommendations at OpenTable

At OpenTable, recommendations play a key role in connecting diners with restaurants. The act of recommending a restaurant to a diner relies heavily on aligning everything we know about the restaurant with everything we can infer about the diner. Our methods go beyond using the diner-restaurant interaction history as the sole input — we use click and search data, the metadata of restaurants, as well as insights gleaned from reviews, together with any contextual information to make meaningful recommendations. In this talk, I will highlight the main aspects of our recommendation stack built with Scala using Apache Spark.

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Making Meaningful Restaurant
Recommendations @OpenTable
Sudeep Das, PhD
Data Scientist
OpenTable
@datamusing
CONFIDENTIAL 2
• Over 32,000 restaurants worldwide
• more than 885 million diners seated since 1998,
representing more than $30 billion spent at partner
restaurants
• Over 17 million diners seated every month
• OpenTable has seated over 254 million diners via a
mobile device. Almost 50% of our reservations are
made via a mobile device
• OpenTable currently has presence in US, Canada,
Mexico, UK, Germany and Japan
• OpenTable has nearly 600 partners including Bing,
Facebook, Google, TripAdvisor, Urbanspoon, Yahoo
and Zagat.
3
OpenTable
the world’s leading provider of online restaurant
reservations
At OpenTable
we aim to power
the best dining
experiences!
Ingredients of a
magical experience
Understanding the diner Understanding the restaurant
Building up a profile of you as a
diner from explicit and implicit
signals - information you have
provided, reviews you have written,
places you have dined at etc.
What type of restaurant is it?
What dishes are they known for?
Is it good for a date night/ family
friendly/ has amazing views etc.
What’s trending?
Connecting the dots
we have a wealth
of data
32 million reviews
diner
requests and
notes
menus
external
ratings,
searches and
transactions
images

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Recsys 2015: Making Meaningful Restaurant Recommendations at OpenTable

  • 1. Making Meaningful Restaurant Recommendations @OpenTable Sudeep Das, PhD Data Scientist OpenTable @datamusing
  • 3. • Over 32,000 restaurants worldwide • more than 885 million diners seated since 1998, representing more than $30 billion spent at partner restaurants • Over 17 million diners seated every month • OpenTable has seated over 254 million diners via a mobile device. Almost 50% of our reservations are made via a mobile device • OpenTable currently has presence in US, Canada, Mexico, UK, Germany and Japan • OpenTable has nearly 600 partners including Bing, Facebook, Google, TripAdvisor, Urbanspoon, Yahoo and Zagat. 3 OpenTable the world’s leading provider of online restaurant reservations
  • 4. At OpenTable we aim to power the best dining experiences!
  • 5. Ingredients of a magical experience Understanding the diner Understanding the restaurant Building up a profile of you as a diner from explicit and implicit signals - information you have provided, reviews you have written, places you have dined at etc. What type of restaurant is it? What dishes are they known for? Is it good for a date night/ family friendly/ has amazing views etc. What’s trending? Connecting the dots
  • 6. we have a wealth of data 32 million reviews diner requests and notes menus external ratings, searches and transactions images
  • 8. diner-restaurant Interactions restaurant metadata The basic ingredients user metadata ratings|searches|reviews … cuisine|price range|hours|topics … user profile
  • 9. There are various approaches to making meaningful recommendations Nearest neighbor approaches in user-user or item-item space Collaborative Filtering based on explicit/implicit interactions Content-based approach leveraging restaurant metadata Factorization machines that include interactions, metadata, as well as context.
  • 13. Matrix Factorization: Implicit preferences Restaurant_1 Restaurant_2 … Restaurant_M Diner_1 50 ? … 100 Diner_2 ? 1 … ? … … … … Diner_N 3 30 … 1 Implicit Preferences (Hu, Koren, Volinsky 2008) Confidence Matrix Binary Preference Matrix
  • 14. 14 Ensemble parameter is a function of the user support Purely Similarity Purely Model based Weighted mean inverse rank ¯a = ↵ 1 r1 + (1 ↵) 1 r2
  • 15. 15
  • 16. Mining the wealth of textual data for cold start and beyond …
  • 17. Content Based Approach • Comes in very handy for cold start where users have very few interactions Very useful for cold start where users have very few interactions. Given a few interactions we can find similar restaurants. Bayesian information retrieval approach. Content based approach
  • 18. 18 Our reviews are rich and verified, and come in all shapes and sizes Superb! This really is a hidden gem and I'm not sure I want to share but I will. :) The owner, Claude, has been here for 47 years and is all about quality, taste, and not overcharging for what he loves. My husband and I don't often get into the city at night, but when we do this is THE place. The Grand Marnier Souffle' is the best I've had in my life - and I have a few years on the life meter. The custard is not over the top and the texture of the entire dessert is superb. This is the only family style French restaurant I'm aware of in SF. It also doesn't charge you an arm and a leg for their excellent quality and that also goes for the wine list. Soup, salad, choice of main (try the lamb shank) and choice of dessert - for around $42 w/o drinks. Many restaurants have thousands of reviews.
  • 19. Word2Vec: Word Embeddings [1] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient Estimation of Word Representations in Vector Space. In Proceedings of Workshop at ICLR, 2013. [2] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean. Distributed Representations of Words and Phrases and their Compositionality. In Proceedings of NIPS, 2013. [3] Tomas Mikolov, Wen-tau Yih, and Geoffrey Zweig. Linguistic Regularities in Continuous Space Word Representations. In Proceedings of NAACL HLT, 2013. “We've [been here for afternoon tea multiple times, and each time] we find it very pleasant” [ 0.00513298, 0.10313627, 0.0773475 , ..., -0.07634512, 0.00877244, 0.04441034]Vec[tea]= ‘teas', ‘empress', ‘scones', ‘iced’, 'fortnum', ‘salon', ‘teapot', ‘teapots', ‘savories', ‘afternoon', ‘earlgrey' …. model.most_similar(‘tea’ ):
  • 21. 21 Early explorations with word2vec: pairings Halibut: Chardonnay Lamb: ?
  • 22. 22 Early explorations with word2vec: pairings Halibut: Chardonnay Lamb: Zinfandel
  • 23. 23 Early explorations with word2vec: pairings Halibut: Chardonnay Lamb: Zinfandel
  • 24. 24 Sushi of Gari, Gari Columbus, NYC Masaki Sushi Chicago Sansei Seafood Restaurant & Sushi Bar, Maui A restaurant like your favorite one but in a different city. Find the “synonyms” of the restaurant in question, then filter by location! Akiko’s, SF San Francisco Maui Chicago New York ' Downtown upscale sushi experience with sushi bar
  • 25. 25 Harris’ Steakhouse in Downtown area ~v(Harris’) + ~v(jazz) Broadway Jazz Club Steakhouse with live jazz ~v(Harris’) + ~v(patio) ~v(Harris’) + ~v(scenic) Celestial Steakhouse Steakhouse with a view Patio at Las Sendas Steakhouse with amazing patio Translating restaurants via concepts
  • 27. 27 We expect diner reviews to be broadly composed of a handful of broad themes Food & Drinks Ambiance Service Value for Money Special occasions This motivated diving into the reviews with topic modeling
  • 28. 28 We applied non- negative matrix factorization to learn topics … • stopword removal • vectorization • TFIDF • NNMF
  • 29. 29 Topics fell nicely into categories DrinksFood Ambiance
  • 30. 30 Topics fell nicely into categories ServiceValue Occasions
  • 31. Our topics reveal the unique aspects of each restaurant without having to read the reviews … Each review for a given restaurant has certain topic distribution Combining them, we identify the top topics for that restaurant. 0 0.5 1 Topic 01 Topic 02 Topic 03 Topic 04 Topic 05 0 0.5 1 Topic 01 Topic 02 Topic 03 Topic 04 Topic 05 0 0.5 1 Topic 01 Topic 02 Topic 03 Topic 04 Topic 05 review 1 review 2 review N . . . 0 0.5 1 Topic 01 Topic 02 Topic 03 Topic 04 Topic 05 Restaurant
  • 32. Looking at the topics and the top reviews associated with it , we know Espetus Churrascaria is not just about meat and steak, but has good salad as well! The service is top notch, its kid friendly, and people go for special occasions, …
  • 33. Content Based Approach • Comes in very handy for cold start where users have very few interactions Very useful for cold start where users have very few interactions. Given a few interactions we can find similar restaurants. Bayesian information retrieval approach. Content based approach + Topic Weights
  • 34. Adding value beyond just making the recommendation
  • 35. 35 We leveraged food and drink related topics to expand our corpus of dishes and drinks Most dishes are usually 1-grams (“tiramisu”) 2-grams (“pork cutlets”) or 3-grams (“lemon ricotta pancake”) For each restaurant, we perform an N-gram analysis of the reviews within the scope of food topics and surface candidate dish tags We were able to generate several thousands of dish tags using this methodology!
  • 37. 37 Sentiments - we use ratings as labels for positive and negative sentiments Ingredients of a stellar experience
  • 38. 38 Sentiments - we use ratings as labels for positive and negative sentiments Ingredients of a terrible experience
  • 39. 39 The model knows that “to die for”, “crispy”, “moist” are actually indicative of positive sentiment when it comes to food! •The lobster and avocado eggs Benedict are to die for. • We finished out meal with the their blackberry bread pudding which was so moist and tasty. •The pork and chive dumplings were perfectly crispy and full of flavor. •I had the Leg of Lamb Tagine and it was "melt in-your-mouth" wonderful. •… we did our best with the scrumptious apple tart and creme brulee. •My husband's lamb porterhouse was a novelty and extremely tender. •We resisted ordering the bacon beignets but gave in and tried them and were glad we did---Yumm! …
  • 40. 40
  • 41. 41 We also learn restaurant specific attributes from review text We learn features using one vs. all Logistic Regression with L1 regularization via a mech turk curated labeled set. For outdoor seating features include obvious ones such as ‘outdoor’, ‘patio’, as well as ‘raining’, ‘sunny’, ‘smoke’, etc. …
  • 42. 42 Dish+Attribute tags and topics can be used to enhance user profiles
  • 43. • Rendle (2010) www.libfm.org Including everything + context: Factorization Machines W ORK IN PROGRESS