Using Data Science to Transform OpenTable Into Your Local Dining Expert
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Presentation for Spark Summit 2015, San Francisco https://spark-summit.org/2015/events/using-data-science-to-transform-opentable-into-your-local-dining-expert/
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New data format: ETL with Spark](https://image.slidesharecdn.com/sparksummit2015-151029184532-lva1-app6891/85/Using-Data-Science-to-Transform-OpenTable-Into-Your-Local-Dining-Expert-10-320.jpg)
Using Data Science to Transform OpenTable Into Your Local Dining Expert
- 1. Using Data Science to Transform OpenTable Into Your Local Dining Expert Pablo Delgado @pablete Sudeep Das @datamusing
- 2. Over 32,000 restaurants worldwide more than 760 million diners seated since 1998, representing more than $30 billion spent at partner restaurants Over 16 million diners seated every month OpenTable has seated over 190 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 Facebook, Google, TripAdvisor, Urbanspoon, Yahoo and Zagat. 2 OpenTable the worlds leading provider of online restaurant reservations
- 3. At OpenTable we aim to power the best dining experiences!
- 4. 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. Whats trending? Connecting the dots
- 5. we have a wealth of data 30 million reviews diner requests and notes menus external ratings, searches and transactions images
- 7. 7 diner-restaurant Interactions restaurant metadata The basic ingredients user metadata ratings|searches|reviews cuisine|price range|hours|topics user profile
- 9. 9 We are mining log data all the way back to 2010 with Spark The result is to compute the number of times each user has searched for a restaurant. SingleSearches(timestamp, rid, uid, reservationSlot, partySize) 2014-01-01 08:00:13 W3SVC1554668004 10.20.20.77 GET /opentables.aspx t=rest&r=11671&m=212&p=2&d=12/31/2013%2011:30:00%20PM&scpref=100 2020 - 64.145.88.235 Mozilla/5.0+(iPad;+CPU+OS+7_0_4+like+Mac+OS+X)+AppleWebKit/537.51.1+(KHTML,+like+Gecko)+Version/7.0+Mobile/11B554a+Safari/ 9537.53GCSCU_1011230515163_H2=C=1011230515163.apps.googleusercontent.com:S=9cbd7893239881e8ac1f50262ff8508463c7b337.gSm24muLAXog5z3_.c2f1:I= 1388563131:X=1388649531;+__qca=P0-1372117488-1388563123763;+aCKE=e1722c4c-644b-413e-8173- d9d5288b1682;+ftc=x=01%2f01%2f2014+11%3a00%3a10&p1=100&p1q=m%3d212%26mn%3d1075%26ref%3d13573%26sp %3dppc_b_Anchorage_nontm&rf1=13573&rf2=13573&er=11671 302 0 0 Old data format: ETL with Spark
- 11. 11 KafkaUsers backups Query/Processing Layer Spark SQL Spark Streaming Spark MLlib JSON Data Products
- 12. 12 http://en.wikipedia.org/wiki/File:Collaborative_Filtering_in_Recommender_Systems.jpg There are various approaches to making meaningful recommendations
- 13. 13 Neighborhood-based CF Item/Item or Restaurant Similarity Item-Based Collaborative Filtering Recommendation Algorithms (Sarwar et al., 2001)
- 14. 14 We use the Jaccard Index as a measure of similarity J(A, B) = |A B| |A [ B| + where is a shrinkage parameter to penalize item pairs that have too few users.
- 16. 16
- 17. 17 Matrix Factorization: Explicit ratings Restaurant_1 Restaurant_2 Restaurant_M Diner_1 5 ? 3 Diner_2 ? 3 ? Diner_N 4 4 1 Explicit Case: Use ratings U V ' Ndiners Nfactors Nfactors X Nrestaurants Matrix Factorization
- 18. 18 Matrix Factorization: Implicit preferences Restaurant_1 Restaurant_2 Restaurant_M Diner_1 50 ? 100 Diner_2 ? 1 ? Diner_N 3 30 1 Collaborative Filtering for Implicit Feedback Datasets (Hu, Koren, Volinsky 2008) Confidence Matrix Binary Preference Matrix
- 19. 19 Matrix Factorization: Implicit preferences Collaborative Filtering for Implicit Feedback Datasets (Hu, Koren, Volinsky 2008)
- 20. 20 Evaluation / Hyperparameter tuning with Precison@K Interactions Users Holdout set Training set
- 21. 21 Hyperparameter tuning using Precision@K Precision@8 = 3/8 1 m mX i=1 Precision@K(i) Precision@K = 1 K |topK holdout|
- 22. 22 Ensemble of item-similarity and MF- based recommendations Weighted mean inverse rank 俗a = 1 r1 + (1 ) 1 r2
- 23. 23 Ensemble parameter is a function of the user support Purely Item Similarity Purely Model based
- 24. 24
- 26. 26 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 Sou鍖le' 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.
- 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 approached the problem from the point of view of summarizing each restaurant using its reviews Analyze the corpus of reviews in a geographic region to learn topics Classify topics into categories (food, ambiance, service etc) Map topics back to restaurants For each restaurant and a topic, surface relevant reviews
- 29. 29 We applied non- negative matrix factorization to learn topics stopword removal vectorization TFIDF NNMF
- 30. 30 and obtained a pretty clean set of topics
- 31. 31 Topics fell nicely into categories DrinksFood Ambiance
- 32. 32 Topics fell nicely into categories ServiceValue Occasions
- 34. 34 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.
- 35. 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,
- 36. 36 while Fog Harbor Fish House is not just about fish, crab and local seafood, but boasts an excellent view of the bay, specially during sunset!
- 37. 37 Rendle (2010) www.libfm.org Including everything + context: Factorization Machines W ORK IN PROGRESS
- 39. 39 Sentiments - we use ratings as labels for positive and negative sentiments Ingredients of a stellar experience
- 40. 40 Sentiments - we use ratings as labels for positive and negative sentiments Ingredients of a terrible experience
- 42. 42 We are using nlp+sentiments to surface relevant snippets around tags
- 43. 43 The model knows that to die for, crispy, moist are actually indicative of positive sentiment when it comes to food! The lobster andavocado eggs Benedictare to die for. We finished out meal with the their blackberrybread puddingwhich was so moist and tasty. The pork andchive dumplingswere perfectly crispy and full of flavor. I had the Leg ofLamb Tagineand it was "melt in-your-mouth" wonderful. ≒ we did our best with the scrumptious apple tart andcreme brulee. My husband'slamb porterhousewas a novelty and extremely tender. We resisted ordering thebacon beignetsbut gave in and tried them and were glad we did--- Yumm!
- 45. 45 Wine pairings with Word2Vec ! Halibut: Chardonnay Lamb: ?
- 46. 46 Halibut: Chardonnay Lamb: Zinfandel Wine pairings with Word2Vec !
- 47. 47 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
- 49. 49 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! Akikos, SF San Francisco Maui Chicago New York ' Downtown upscale sushi experience with sushi bar
- 50. 50 nigiri sashimi gari maki roku rolls roll godzilla chirashi robata zushi omakase yellowtail unagi samba toro gyoza aburi spider starburst nakazawa shabu sasa katana sake hapa maguro tsunami raku kappo yasuda otoro seki tamari ra teppanyaki caterpillar japan shashimi hamasaku Early explorations with Word2vec: Find synonyms for Sushi We use Apache Sparks Implementation of Word2Vec (skip-gram model)
- 52. 52 This restaurant is SO LOUD we couldnt speak across our table. The food was okay, but the restaurant was so noisy that no conversation during diner was possible. It was a little loud because of the number of tables and hard surfaces but we were able to hear each other. An obnoxiously loud and impersonal restaurant, churning out uninteresting, tasteless food. This place was busy and extremely loud - very hard to carry on a conversation here. If only the noise level was lowered this would be the best restaurant in SF. This restaurant has a lot to offer but easy conversation is not there. I asked for a round table and was given a rectangle table in a corner which made talking difficult. Was squeezed into a corner with little room beside the adjoining table, which had the loudest couple in SF, so communication was near impossible, coupled with a crowded restaurant. Always hesitated trying the new location as it seemed too loud sterile and impersonal.
- 53. 53 They were good, but I can get better spring rolls at almost any vietnamese restaurant in my neighborhood - nothing to deserve all the hype. Appetizers were great (spring rolls particularly tasty), but the entrees were only "very good." As someone who has eaten at many Asian restaurants, I would say that the food here is good but nothing outstanding - definitely not worth the hype. Food was very good, but there isn't an amazing difference in flavors than what you wouldn't get at an authentic Vietnamese-style restaurant. The food, while generally good, was not substantially better than what we could purchase at a Vietnamese restaurant in our home city for a fifth of the cost. Restaurant receives a lot of hype, but does not live up to its culinary reputation. I am sure there are many equally good Vietnamese restaurants in San Francisco that are 1/2 the costs. The food was good but not something I would expect for those prices on the hype. I've had very good vietnamese food, but at a much lower cost. There was so much hype about Slanted Door that I was expecting an incredible dining experience but instead it was just good.
- 54. 54 Not only was the food wonderful, but the staff attentive, knowledgable and very accommodating. Not only was the food appetizing; the staff was particularly attentive and cordial! The staff was friendly and our food came at all the right times. The atmosphere was wonderful, the food was incredible, and the service couldn't have been better. The staff was so helpful in customizing our food requirements with the specific food restrictions and size of our party. Excellent food and waitress was very helpful in knowing how much food to order and made excellent recommendations. The many wonderful flavors and presentation of the food assures my return. The food is wonderful and the view of the Bay and the bridge are wonderful. We had a great table with a view, the service was very attentive, our waiter gave wonderful suggestions.