Nomao: data analysis for personalized local search

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Material presented at the EC FET Open project NADINE review (2013), Université Paul Sabatier, Toulouse, France.
Institution: Nomao

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Nomao: data analysis for personalized local search

  1. 1. Data analysis for personalized local search Estelle Delpech Chief Science Officer EC FET Open project NADINE review ´ Universite Paul Sabatier 14th november 2013
  2. 2. Plan 1. Nomao : book of good places (between friends) 2. Data analysis @ nomao 3. Ongoing projects
  3. 3. Nomao book of good places (between friends) www.nomao.com Web and mobile applications Find, keep and share good places (restaurants, bars, shopping, doctors...) Personalized search : recommendation, geolocation 1 / 15
  4. 4. Web application Off-line user : e-reputation On-line user (FB) : recommendation – places that matches your tastes – places recommended by your friends 2 / 15
  5. 5. Mobile application E-reputation Recommendation Geolocation-based search Augmented reality 3 / 15
  6. 6. Nomao ´ Toulouse / Paris / Evreux / Nantes / Chartres... 10 employees 2007 creation 2010 acquired by Ebuzzing Business model : premium-rate numbers 2012 3 M visits / day ⇒ ECML, EGC, TALN, INFORSID, VSST, ICEIS, IEEE TNNLS ... ⇒ http://www.nomao.com/labs 4 / 15
  7. 7. Plan 1. Nomao : book of good places (between friends) 2. Data analysis @ nomao 3. Ongoing projects
  8. 8. Data analysis @ nomao 5 / 15
  9. 9. Data analysis @ nomao 5 / 15
  10. 10. Data analysis @ nomao 5 / 15
  11. 11. Data analysis @ nomao 5 / 15
  12. 12. Places database creation 6 / 15
  13. 13. Places database creation 6 / 15
  14. 14. Data extraction SOURCE 1  SOURCE 2  ´ : Les Caves de La Marechale  TAGS : restaurant     ADDRESS :     street :     city : Toulouse     COMMENTS :     rating : 4  text : ”I enjoyed most of the...” NAME 7 / 15   ´ : Caves de La Marechale SARL  TAGS : french     ADDRESS :     street : Rue Jules Chalande     city : Toulouse     COMMENTS :     rating : 2  text : ”Didn’t really liked the...” NAME
  15. 15. Data integration PLACE #5237890  ´ : Les Caves de La Marechale  TAGS : restaurant, french    ADDRESS : street : Rue Jules Chalande  city : Toulouse   rating : 4, text : ”I enjoyed most of the...” COMMENTS : rating : 2, text : ”Didn’t really like the...” NAME 8 / 15        
  16. 16. Data analysis PLACE #5237890 ´ : Les Caves de La Marechale  TAGS : restaurant, french   CATEGORY : eating restaurant european french    ADDRESS : street : Rue Jules Chalande  city : Toulouse   station : Capitole, distance : 304m  SUBWAY :  station : Esquirol, distance : 192m    COMMENTS : rating : 4, text : ”I enjoyed most of the...”  rating : 2, text : ”Didn’t really like the...”    POSITIVE FEATURES : SERVICE : great staff  DISHES : delicious chocolate cake E - REPUTATION : 79%  NAME 9 / 15                    
  17. 17. Generated content 10 / 15
  18. 18. Natural language description (translation) ´ ` R ESTAURANT L ES CAVES D E L A M AR E CHALE A TOULOUSE ´ The restaurant Les Caves de La Marechale in Toulouse invites you to enjoy the best specialties of South-West France. This restaurant is located Rue Jules Chalande in Toulouse. Esquirol or Capitole are the closest subway stations to the restaurant. You will have the possibility to enjoy an elegant atmosphere. ´ At Les Caves de La Marechale, credit card payment is ´ accepted. Close to Les Caves de La Marechale, you will find the restaurant CAMUZET Georges Jean Andre, the restaurant La Corde, the restaurant BMA and the restaurant Le Petit Lord. 11 / 15
  19. 19. Places recommendation 12 / 15
  20. 20. Places recommendation E-reputation sentiment analysis + ratings 12 / 15
  21. 21. Places recommendation E-reputation sentiment analysis + ratings Affinity between place and user 12 / 15
  22. 22. Places recommendation E-reputation sentiment analysis + ratings Affinity between place and user collaborative filtering : places liked by people who like the same places as the user 12 / 15
  23. 23. Places recommendation E-reputation sentiment analysis + ratings Affinity between place and user collaborative filtering : places liked by people who like the same places as the user descriptive profiling : places that have the same tags as the places liked by the user 12 / 15
  24. 24. Places recommendation E-reputation sentiment analysis + ratings Affinity between place and user collaborative filtering : places liked by people who like the same places as the user descriptive profiling : places that have the same tags as the places liked by the user Social recommendation places liked by the user’s friends 12 / 15
  25. 25. Search and ranking 13 / 15
  26. 26. Search and ranking Multi-criteria ranking : 13 / 15
  27. 27. Search and ranking Multi-criteria ranking : Query ↔ place similarity 13 / 15
  28. 28. Search and ranking Multi-criteria ranking : Query ↔ place similarity Geographical proximity 13 / 15
  29. 29. Search and ranking Multi-criteria ranking : Query ↔ place similarity Geographical proximity Content quality 13 / 15
  30. 30. Search and ranking Multi-criteria ranking : Query ↔ place similarity Geographical proximity Content quality E-reputation 13 / 15
  31. 31. Search and ranking Multi-criteria ranking : Query ↔ place similarity Geographical proximity Content quality E-reputation Affinity between place and user 13 / 15
  32. 32. Search and ranking Multi-criteria ranking : Query ↔ place similarity Geographical proximity Content quality E-reputation Affinity between place and user Social recommendation 13 / 15
  33. 33. Plan 1. Nomao : book of good places (between friends) 2. Data analysis @ nomao 3. Ongoing projects
  34. 34. Ongoing projects 14 / 15
  35. 35. Ongoing projects Learning-to-rank ranking model learnt from users’ actions (Ph. D. thesis) 14 / 15
  36. 36. Ongoing projects Learning-to-rank ranking model learnt from users’ actions (Ph. D. thesis) Data fusion source A → 05.61.23.89.88 source B → 05.62.48.33.90 which no is right ? 14 / 15
  37. 37. Ongoing projects Learning-to-rank ranking model learnt from users’ actions (Ph. D. thesis) Data fusion source A → 05.61.23.89.88 source B → 05.62.48.33.90 which no is right ? Text mining Name, address, phone number, etc. could be directly extracted from the businesses websites 14 / 15
  38. 38. // Contact estelle@nomao.com // News www.blog.nomao.fr

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