Ranked-Restaurant Searching System using Data Mining
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This document describes a ranked restaurant search system using data mining and machine learning techniques. It involves 4 main steps: 1) Creating a database of restaurant information scraped from websites. 2) Collecting blog data related to restaurant reviews. 3) Extracting relevant information from blogs using natural language processing. 4) Building a search engine using Apache Solr to allow users to search and rank restaurants based on analysis of blog data. Machine learning classifiers like Naive Bayes and SVMs are used to determine if blogs contain restaurant reviews.
![url = “http://place.map.daum.net/15935749”
handle = urllib2.urlopen(url)
data = handle.read()
soup = BeautifulSoup(data)
#Name Crawl
name = soup.title.string
name = name.split("–")[0]
#Address1 Crawl
address1 = soup.find_all("dd", "desc")[0].get_text()
address1 = address1.strip()
from bs4 import BeautifulSoup](https://image.slidesharecdn.com/restaurantsearch-190331140344/85/Ranked-Restaurant-Searching-System-using-Data-Mining-12-320.jpg)
Ranked-Restaurant Searching System using Data Mining
- 1. Ranked-Restaurant Searching System using Data Mining: Bob is my friend Han Sang Jun / Yoon Seung Hyun 14 – 2 Capstone Design Handong University
- 2. How can we find the most popular restaurant for Pasta in Upland?
- 3. Not user’s feedback But data already existing on WEB! such as blog review for restaurant...
- 8. Make database for all restaurants info in Korea Step 1 1
- 9. Possession 19.89% for WEB search in Korea http://place.map.daum.net/15935749
- 10. <html> <body> <tittle>restaurant_name</title> <div> <span class=“t_l”>address</span> <span class=“t_l num”>phone</span> <span class=“f_l”>category > Korean</span> </div> <div> <dt class=“list_info”>available to use card</dl> <dt class=“list_info”>available for reservation</dl> . . </div> </body> </html>
- 11. Python Library – BeautifulSoup4 You can move, find, extract info on HTML structure
- 12. url = “http://place.map.daum.net/15935749” handle = urllib2.urlopen(url) data = handle.read() soup = BeautifulSoup(data) #Name Crawl name = soup.title.string name = name.split("–")[0] #Address1 Crawl address1 = soup.find_all("dd", "desc")[0].get_text() address1 = address1.strip() from bs4 import BeautifulSoup
- 13. for i in range (00000000, 99999999): if category == Restaurant: Insert information in MySQLdb http://place.map.daum.net/15935749
- 14. Name Address Phone Category Description Longitude Langtitude Database for all restaurants in Korea Google GeoCoding
- 15. Collect blogs related to restaurant review Step 2 2
- 16. Possession 76.89% for WEB search in Korea http://section.blog.naver.com/ Popular Blog service
- 17. http://section.blog.naver.com/sub/SearchBlog.nhn? type=post&option.keyword=포항%20환여횟집 Database for all restaurants in Korea Name Address Phone Category Description Longitude Langtitude
- 18. Get blogs info with BeautifulSoup4 by getting href link
- 19. But does search engine show exact results to us? If not...
- 20. Let computer learn to collect appropriate blog’s data with results preview
- 21. What is machine learning? vague, ambiguous
- 22. Make Learning Set(training Set) Training Test Set
- 23. • Supervised Learning Someone gives us examples and the right answer • Unsupervised Learning We see examples but get no feedback We need to find patterns in the data • Semi-supervised Learning Given a small number of examples with the right answers, we need to find patterns in the data, so that we can predict the right answer for unseen examples • Reinforcement Learning We take actions and get rewards Have to learn how to get high rewards Different Kinds of Learning
- 24. Make learning set with results preview for judging whether it is related to restaurant review or not We need to classify them!
- 25. Classifier – Supervised learning 1. K-nearest neighbors algorithm 2. Perceptron 3. Bayesian approach 4. Decision trees 5. Neural Networks 6. Support Vector Machines 7. Ensembles
- 26. Is_Restaurant Is_Not_Restaurant restaurant delicious good politic hello economy atmosphere 200 250 300 250 350 200 150 300 word count word count atmosphere 3000 blog previews for related to restaurant review Learning Set
- 27. Is_Restaurant Is_Not_Restaurant restaurant delicious good politic hello economy atmosphere 200 250 300 250 350 200 150 300 word count word count atmosphere 3000 blog previews for related to restaurant review Learning Set
- 28. Is_Restaurant Is_Not_Restaurant restaurant delicious good politic hello economy atmosphere 200 250 300 250 350 200 150 300 word count word count atmosphere Learning Set Using morpheme analyzer eat, ate, eaten are changed to eat
- 29. “It was good to eat delicious pasta in this restaurant!” Is this related to restaurant review? with upper table...
- 30. Naïve Bayes
- 31. possibility that a given sentence is in category of Is_Restaurant possibility that a given sentence is in category of Is_Not_Restaurant Compare!
- 32. P(Is_Restaurant | sentence) Compare! P(Is_Not_Restaurant | sentence)
- 33. Compare! p(Is | s) p(s | Is) * p(s) p(Is) p(IsN | s) p(s | IsN) *p(IsN) p(s)
- 34. p(Is | s) p(s | Is) *p(Is) p(s) p(ls) : possibility of choosing Is_Restaurant(assume as 0.5) p(s | ls) : In Is_Restaurant set possibility of choosing the given sentece
- 35. p(Is | s) p(s | Is) * p(Is) p(s) p(s | ls) : In Is_Restaurant set, possibility of choosing the given sentence p(s | Is) = p(word | Is) * p(word | Is) * p(word | Is)… We assume that each word cannot affect to each other...
- 36. Is_Restaurant “It was good to eat delicious pasta in this restaurant!” p(Is) = 0.5 p(restaurant | Is) = 1000 250 p(delicious | Is) = 1000 300 p(good | Is) = 1000 250 p(eat | Is) = 1000 0(1) restaurant delicious good 200 250 300 250 word count atmosphere
- 37. “It was good to eat delicious pasta in this restaurant!” 0.5 * 0.001 * 0.3 * 0.25 * 0.25 = 9.375 * 10^-6 Is_Restaurant restaurant delicious good 200 250 300 250 word count atmosphere
- 38. 0.001 ^ 4 = 1 * 10^-12 “It was good to eat delicious pasta in this restaurant!” Is_Not_Restaurant politic hello economy atmosphere 350 200 150 300 word count
- 39. This is related to restaurant review! with upper table... “It was good to eat delicious pasta in this restaurant!”
- 42. We need to find optimal hyperplane
- 43. Extract meaningful info from the collected blogs Step 3 3
- 44. Title Date Content Reply URL Database for Blogs For restaurant ranking
- 45. Title Date Content Reply URL Database for Blogs Point for one blog review = (date * 0.5) + (the number of reply * 0.5) Point for one restaurant = the average of reviews’ point
- 46. Food_Dictionary Area_Dictionary If a word is represented in dictionary over 100 times Title Date Content Reply URL Database for Blogs
- 47. from WEB encyclopedia Food_Dictionary Area_Dictionary
- 48. Restaurant_Info Blog Extract info from Blogs Table Left Join Final Output Extracting Database Set
- 49. For user’s convenience, Search engine needed Step 4 4
- 50. Using “Apache Solr” that popular, fast open source enterprise search platform from the Apache Lucene project. Nutch Solr Lucene Hadoop
- 52. solrconfig.xml
- 53. data-config.xml
- 54. What we want to develop... Improve filtering blog performance On Ruby on Rails framework, using BlackLight served as library to use Solr in Ruby on Rails Front-End Design using HTML5, CSS3, Javascript, Jquery... New Ranking Algorithm
- 55. Point of Restaurant = Points of Blogger = Recency, Frequency, Density Recency, Frequency, People Based on Blogger’s action New Ranking Algorithm
- 58. How can we find the most popular restaurant for Pasta in Upland?
- 59. Ask Bob!
- 60. ThankYou!
- 61. 서가앤쿡 환여횟집 빕스 설빙 뚝배기 이탈리아 삼겹살 맛집 냉면 맛집 샤브샤브 맛집 보리밥 맛집 초밥 맛집 99% 96% 93% 100% 98% 100% 100% 100% 100% 100% 100 blogs related to Is_Restaurant Performance For one keywords => 10 blogs
- 62. 정치 경제 문화 사회 한동대학교 소녀시대 중앙일보 한겨례 성경 기독교 59% 66% 45% 65% 42% 81% 67% 62% 54% 58% 100 blogs related to Is_Not_Restaurant Performance For one keywords => 10 blogs