![Codificando o Mahantan
def manhattan(rating1, rating2):
"""Computes the Manhattan distance. Both rating1 and rating2 are
dictionaries of the form {'The Strokes': 3.0, 'Slightly
Stoopid': 2.5}"""
distance = 0
commonRatings = False
for key in rating1:
if key in rating2:
distance += abs(rating1[key] – rating2[key])
commonRatings = True
if commonRatings:
return distance
else:
return -1 #Indicates no ratings in common](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-54-320.jpg)
![Codificando o Mahantan
def manhattan(rating1, rating2):
"""Computes the Manhattan distance. Both rating1 and rating2 are
dictionaries of the form {'The Strokes': 3.0, 'Slightly
Stoopid': 2.5}"""
distance = 0
commonRatings = False
for key in rating1:
if key in rating2:
distance += abs(rating1[key] – rating2[key])
commonRatings = True
if commonRatings:
return distance
else:
return -1 #Indicates no ratings in common](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-56-320.jpg)
![Codificando o Mahantan
def manhattan(rating1, rating2):
"""Computes the Manhattan distance. Both rating1 and rating2 are
dictionaries of the form {'The Strokes': 3.0, 'Slightly
Stoopid': 2.5}"""
distance = 0
commonRatings = False
for key in rating1:
if key in rating2:
distance += abs(rating1[key] – rating2[key])
commonRatings = True
if commonRatings:
return distance
else:
return -1 #Indicates no ratings in common
>>> manhattan(users['Hailey'], users['Veronica'])
2.0
>>> manhattan(users['Hailey'], users['Jordyn'])
1.5
>>>](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-57-320.jpg)
![Codificando Euclidean
def euclidean(rating1, rating2):
"""Computes the euclidean distance.
Both rating1 and rating2 are dictionaries of the form
{'The Strokes': 3.0, 'Slightly Stoopid': 2.5}"""
distance = 0.0
commonRatings = False
for key in rating1:
if key in rating2:
distance += pow(abs(rating1[key] - rating2[key]), 2.0)
commonRatings = True
if commonRatings:
return pow(distance, 1/2.0)
else:
return -1 #Indicates no ratings in common](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-59-320.jpg)
![Codificando Euclidean
def euclidean(rating1, rating2):
"""Computes the euclidean distance.
Both rating1 and rating2 are dictionaries of the form
{'The Strokes': 3.0, 'Slightly Stoopid': 2.5}"""
distance = 0.0
commonRatings = False
for key in rating1:
if key in rating2:
distance += pow(abs(rating1[key] - rating2[key]), 2.0)
commonRatings = True
if commonRatings:
return pow(distance, 1/2.0)
else:
return -1 #Indicates no ratings in common
1.4142135623730951](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-61-320.jpg)
![Codificando Euclidean
def euclidean(rating1, rating2):
"""Computes the euclidean distance.
Both rating1 and rating2 are dictionaries of the form
{'The Strokes': 3.0, 'Slightly Stoopid': 2.5}"""
distance = 0.0
commonRatings = False
for key in rating1:
if key in rating2:
distance += pow(abs(rating1[key] - rating2[key]), 2.0)
commonRatings = True
if commonRatings:
return pow(distance, 1/2.0)
else:
return -1 #Indicates no ratings in common
>>> euclidean(users['Hailey'], users['Veronica'])
1.4142135623730951](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-62-320.jpg)
![Find the closest users
def computeNearestNeighbor(username, users):
"""creates a sorted list of users based on their distance to
username"""
distances = []
for user in users:
if user != username:
distance = manhattan(users[user], users[username])
distances.append((distance, user))
# sort based on distance -- closest first
distances.sort()
return distances](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-64-320.jpg)
![Find the closest users
>>> computeNearestNeighbor('Hailey', users)
[(2.0, 'Veronica'), (4.0, 'Chan'),(4.0, 'Sam'), (4.5, 'Dan'), (5.0,
'Angelica'), (5.5, 'Bill'), (7.5, 'Jordyn')]
>>>](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-66-320.jpg)
![Find the closest users
def computeNearestNeighbor(username, users):
"""creates a sorted list of users based on their distance to
username"""
distances = []
for user in users:
if user != username:
distance = manhattan(users[user], users[username])
distances.append((distance, user))
# sort based on distance -- closest first
distances.sort()
return distances
>>> computeNearestNeighbor('Hailey', users)
[(2.0, 'Veronica'), (4.0, 'Chan'),(4.0, 'Sam'), (4.5, 'Dan'), (5.0,
'Angelica'), (5.5, 'Bill'), (7.5, 'Jordyn')]
>>>](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-67-320.jpg)
![The recommender
def recommend(username, users):
"""Give list of recommendations"""
# first find nearest neighbor
nearest = computeNearestNeighbor(username, users)[0][1]
recommendations = []
# now find bands neighbor rated that user didn't
neighborRatings = users[nearest]
userRatings = users[username]
for artist in neighborRatings:
if not artist in userRatings:
recommendations.append((artist, neighborRatings[artist]))
recommendations.sort(key=lambda artistTuple: artistTuple[1],
reverse = True)
return recommendations](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-69-320.jpg)
![The recommender
>>> recommend('Hailey', users)
[('Phoenix', 4.0), ('Blues Traveler', 3.0), ('Slightly Stoopid', 2.5)]
>>> recommend('Chan', users)
[('The Strokes', 4.0), ('Vampire Weekend', 1.0)]
>>> recommend('Angelica', users)
[]](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-71-320.jpg)
![The recommender
def recommend(username, users):
"""Give list of recommendations"""
# first find nearest neighbor
nearest = computeNearestNeighbor(username, users)[0][1]
recommendations = []
# now find bands neighbor rated that user didn't
neighborRatings = users[nearest]
userRatings = users[username]
for artist in neighborRatings:
if not artist in userRatings:
recommendations.append((artist, neighborRatings[artist]))
recommendations.sort(key=lambda artistTuple: artistTuple[1],
reverse = True)
return recommendations
>>> recommend('Hailey', users)
[('Phoenix', 4.0), ('Blues Traveler', 3.0), ('Slightly Stoopid', 2.5)]
>>> recommend('Chan', users)
[('The Strokes', 4.0), ('Vampire Weekend', 1.0)]
>>> recommend('Angelica', users)
[]](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-72-320.jpg)
](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-74-320.jpg)
![The recommender
def recommend(username, users):
"""Give list of recommendations"""
# first find nearest neighbor
nearest = computeNearestNeighbor(username, users)[0][1]
recommendations = []
# now find bands neighbor rated that user didn't
neighborRatings = users[nearest]
userRatings = users[username]
for artist in neighborRatings:
if not artist in userRatings:
recommendations.append((artist, neighborRatings[artist]))
recommendations.sort(key=lambda artistTuple: artistTuple[1],
reverse = True)
return recommendations
>>> computeNearestNeighbor('Angelica', users)
[(3.5, 'Veronica'), (4.5, 'Chan'), (5.0, 'Hailey'), (8.0, 'Sam'), (9.0,
'Bill'), (9.0, 'Dan'), (9.5, 'Jordyn')]('Hailey', users)](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-75-320.jpg)
![Pearson Correlation
def pearson(rating1, rating2):
sum_xy = 0
sum_x = 0
sum_y = 0
sum_x2 = 0
sum_y2 = 0
n = 0
for key in rating1:
if key in rating2:
n += 1
x = rating1[key]
y = rating2[key]
sum_xy += x * y
sum_x += x
sum_y += y
sum_x2 += x**2
sum_y2 += y**2
# now compute denominator
denominator = sqrt(sum_x2 - (sum_x**2) / n) *
sqrt(sum_y2 -(sum_y**2) / n)
if denominator == 0:
return 0
else:
return (sum_xy - (sum_x * sum_y) / n) / denominator](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-84-320.jpg)
![Pearson Correlation
>>> pearson(users['Angelica'], users['Bill'])
-0.90405349906826993
>>> pearson(users['Angelica'], users['Hailey'])
0.42008402520840293
>>> pearson(users['Angelica'], users['Jordyn'])
0.76397486054754316
>>>](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-86-320.jpg)
![Pearson Correlation
def pearson(rating1, rating2):
sum_xy = 0
sum_x = 0
sum_y = 0
sum_x2 = 0
sum_y2 = 0
n = 0
for key in rating1:
if key in rating2:
n += 1
x = rating1[key]
y = rating2[key]
sum_xy += x * y
sum_x += x
sum_y += y
sum_x2 += x**2
sum_y2 += y**2
# now compute denominator
denominator = sqrt(sum_x2 - (sum_x**2) / n) *
sqrt(sum_y2 -(sum_y**2) / n)
if denominator == 0:
return 0
else:
return (sum_xy - (sum_x * sum_y) / n) / denominator
>>> pearson(users['Angelica'], users['Bill'])
-0.90405349906826993
>>> pearson(users['Angelica'], users['Hailey'])
0.42008402520840293
>>> pearson(users['Angelica'], users['Jordyn'])
0.76397486054754316
>>>](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-87-320.jpg)
![Change people to items
{'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5},
'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5}}
to
{'Lady in the Water':{'Lisa Rose':2.5,'Gene Seymour':3.0},
'Snakes on a Plane':{'Lisa Rose':3.5,'Gene Seymour':3.5}} etc.
def transformPrefs(prefs):
result={}
for person in prefs:
for item in prefs[person]:
result.setdefault(item,{})
# Flip item and person
result[item][person]=prefs[person][item]
return result](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-97-320.jpg)
![Change people to items
{'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5},
'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5}}
to
{'Lady in the Water':{'Lisa Rose':2.5,'Gene Seymour':3.0},
'Snakes on a Plane':{'Lisa Rose':3.5,'Gene Seymour':3.5}} etc.
def transformPrefs(prefs):
result={}
for person in prefs:
for item in prefs[person]:
result.setdefault(item,{})
# Flip item and person
result[item][person]=prefs[person][item]
return result
>> movies=recommendations.transformPrefs(recommendations.users)
>> recommendations.computeNearestNeighbors(‘Blues Traveler’, movies)
[(0.657, 'You, Me and Dupree'), (0.487, 'Lady in the Water'), (0.111, 'Snakes on a
Plane'), (-0.179, 'The Night Listener'), (-0.422, 'Just My Luck')]](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-98-320.jpg)
![Change people to items
{'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5},
'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5}}
to
{'Lady in the Water':{'Lisa Rose':2.5,'Gene Seymour':3.0},
'Snakes on a Plane':{'Lisa Rose':3.5,'Gene Seymour':3.5}} etc.
def transformPrefs(prefs):
result={}
for person in prefs:
for item in prefs[person]:
result.setdefault(item,{})
# Flip item and person
result[item][person]=prefs[person][item]
return result](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-101-320.jpg)
![Change people to items
{'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5},
'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5}}
to
{'Lady in the Water':{'Lisa Rose':2.5,'Gene Seymour':3.0},
'Snakes on a Plane':{'Lisa Rose':3.5,'Gene Seymour':3.5}} etc.
def transformPrefs(prefs):
result={}
for person in prefs:
for item in prefs[person]:
result.setdefault(item,{})
# Flip item and person
result[item][person]=prefs[person][item]
return result
>> movies=recommendations.transformPrefs(recommendations.critics)
>> recommendations.computeNearestNeighbors(movies,'Superman Returns')
[(0.657, 'You, Me and Dupree'), (0.487, 'Lady in the Water'), (0.111, 'Snakes on a
Plane'), (-0.179, 'The Night Listener'), (-0.422, 'Just My Luck')]](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-102-320.jpg)
![Find the closest items
def calculateSimilarItems(prefs,sim_distance=manhattan):
! # Create a dictionary of items showing which other items they
! # are most similar to.
! result={}
! # Invert the preference matrix to be item-centric
! itemPrefs=transformPrefs(prefs)
! c=0
! for item in itemPrefs:
! ! # Status updates for large datasets
! ! c+=1
! ! if c%100==0: print "%d / %d" % (c,len(itemPrefs))
# Find the most similar items to this one
scores=computeNearestNeighbor(item,itemPrefs,distance=sim_distance)
result[item]=scores
! return result](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-106-320.jpg)
![Find the closest items
def calculateSimilarItems(prefs,sim_distance=manhattan):
! # Create a dictionary of items showing which other items they
! # are most similar to.
! result={}
! # Invert the preference matrix to be item-centric
! itemPrefs=transformPrefs(prefs)
! c=0
! for item in itemPrefs:
! ! # Status updates for large datasets
! ! c+=1
! ! if c%100==0: print "%d / %d" % (c,len(itemPrefs))
# Find the most similar items to this one
scores=computeNearestNeighbor(item,itemPrefs,distance=sim_distance)
result[item]=scores
! return result
>>> itemsim=recommendations.calculateSimilarItems(users)
>>> itemsim
{'Lady in the Water': [(0.40000000000000002, 'You, Me and Dupree'), (0.2857142857142857, 'The
Night Listener'),... 'Snakes on a Plane': [(0.22222222222222221, 'Lady in the Water'),
(0.18181818181818182, 'The Night Listener'),... etc.](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-107-320.jpg)
![The recommender
def recommend(username,users, similarities, n=3):
scores = {}
totalSim = {}
#
# now get the ratings for the user
#
userRatings = users[username]
# Loop over items rated by this user
for item, rating in userRatings.items():
#Loop over items similar to this one
for sim, other_item in similarities[item]:
# Ignore if this user has already rated this item
if other_item in userRatings: continue
# Weighted sum of rating times similarity
scores.setdefault(other_item, 0.0)
scores[other_item]+= sim * rating
# Sum of all the similarities
totalSim.setdefault(other_item, 0.0)
totalSim[other_item] += sim
# Divide each total score by total weighting to get an average
recommendations = [(score/totalSim[item],item) for item,score in scores.items()]
# finally sort and return
recommendations.sort(key=lambda artistTuple: artistTuple[1], reverse = True)
# Return the first n items
return recommendations[:n]](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-109-320.jpg)
![The recommender
>>> recommend('Hailey', users,similarities,3)
[(3.1176470588235294, 'Slightly Stoopid'),
(2.639207507820647, 'Phoenix'),(2.64476386036961, 'Blues Traveler')]](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-110-320.jpg)
![The recommender
def recommend(username,users, similarities, n=3):
scores = {}
totalSim = {}
#
# now get the ratings for the user
#
userRatings = users[username]
# Loop over items rated by this user
for item, rating in userRatings.items():
#Loop over items similar to this one
for sim, other_item in similarities[item]:
# Ignore if this user has already rated this item
if other_item in userRatings: continue
# Weighted sum of rating times similarity
scores.setdefault(other_item, 0.0)
scores[other_item]+= sim * rating
# Sum of all the similarities
totalSim.setdefault(other_item, 0.0)
totalSim[other_item] += sim
# Divide each total score by total weighting to get an average
recommendations = [(score/totalSim[item],item) for item,score in scores.items()]
# finally sort and return
recommendations.sort(key=lambda artistTuple: artistTuple[1], reverse = True)
# Return the first n items
return recommendations[:n]
>>> recommend('Hailey', users,similarities,3)
[(3.1176470588235294, 'Slightly Stoopid'),
(2.639207507820647, 'Phoenix'),(2.64476386036961, 'Blues Traveler')]](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-111-320.jpg)
![source, the recommendation architecture that we propose will would rely more on collaborative-filtering techniques, that is,
aggregate the results of such filtering techniques. Bezerra and Carvalho proposed approaches where the results
the reviews from similar users.
We aim at integrating the previously mentioned hybrid prod- Figure 1 shows a overview of our meta recommender
achieved showed to be very promising [19].
approach. By combining the content-based filtering and the
uct recommendation approach in a mobile application so the
A.
Crab is already in production
users could benefit from useful and logical recommendations. collaborative-based one into a hybrid recommender system, it
Moreover, we aim at providing a suited explanation for each would use the services/products III. S YSTEM catalogues
repositories which D ESIGN
recommendation to the user, since the current approaches just the services to be recommended, and the review repository
Application data information our mobile recommender sys-
that contains the user opinions about those services. All this for
only deliver product recommendations with a overall score
without pointing out the appropriateness of such recommen- datatembecan be from data source containers in the web product description
can extracted divided into two parts: the rec
dation [13]. Besides the basic information provided by the such(such location-based social network Foursquare its attributes) and the user
as the as location, description and [17] as
Hybrid Meta Approach gives the system’s architecture and
suppliers, the system will deliver the explanation, providing
relevant reviews of similar users, we believe that it will
tags, etc.). The Figure 3
increase the confidence in the buying decision process and the
displayed at the Figure 2 and the location recommendation
engine from Google: Google HotPot [18]. by user (such as rating, comments,
reviews or ratings provided
mo
wh
product accepptance rate. In the mobile context this approach
po
could help the users in this process and showing the user
relative components. thi
opinions could contribute to achieve this task. rec
spe
!"#$"%&'$ 5&-$
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)))))))))))%$4%,5)94,14>?) <',7)41$
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8&=,%*1,'>$
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8&4,99&0731*,0$:0;*0&$ !B#$B*%1$,2$D4,'&7$<',7)41%$
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we
Fig. 2. User Reviews from Foursquare Social Network 8&=,%*1,'>$
com
7"$%)
!"8+99"(2"'))
!8#$830E&7$<',7)41%$
The content-based filtering approach will be used to filter ext
the product/service repository, while the collaborative based
8&%).1%$ B.
approach will derive the product review recommendations. In
addition we will use text mining techniques to distinct the
!"8+99"(2%$,+(#) polarity of the user review between positive or negative one.
This information summarized would contribute in the product Architecture
Fig. 3. Mobile Recommender System rat
score recommendation computation. The final product recom-
Fig. 1. Meta Recommender Architecture
mendation score is computed by integrating the result of both
me
recommenders. By now, weproduct/service recommender, the user could
In our mobile are considering to use different and
Since one of the goals of this work is to incorporate options regarding this integration approach, one and get a list of recommen-
different data sources of user opinions and descriptions, we filter some products or services at special oth
is the symbolic data analysis approach (SDA) [19], which
have addopted an meta recommendation architecture. By using eachtations. The user user ratings/reviews arehis preferences or give his
product description and also can enter modeled ow
a meta recommender architecture, the system would provide
a personalized control over the generated recommendation list
feedback to some offered product recommendation.
as set of modal symbolic descriptions that summarizes the Re
information provided by the corresponding data sources. It is](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-113-320.jpg)
![Distributed Computing with mrJob
https://github.com/Yelp/mrjob
"""The classic MapReduce job: count the frequency of words.
"""
from mrjob.job import MRJob
import re
WORD_RE = re.compile(r"[w']+")
class MRWordFreqCount(MRJob):
def mapper(self, _, line):
for word in WORD_RE.findall(line):
yield (word.lower(), 1)
def reducer(self, word, counts):
yield (word, sum(counts))
if __name__ == '__main__':
MRWordFreqCount.run()
It supports Amazon’s Elastic MapReduce(EMR) service, your own Hadoop cluster or
local (for testing)
http://aimotion.blogspot.com/2012/08/introduction-to-recommendations-with.html](https://image.slidesharecdn.com/recsysintroduction-130225232248-phpapp02/85/Construindo-Sistemas-de-Recomendacao-com-Python-119-320.jpg)
Uploaded byMarcel Caraciolo
Construindo Sistemas de Recomendação com Python
The document discusses recommendation systems using Python, highlighting their importance in managing information overload and suggesting relevant content to users. It covers various methods like content-based filtering, collaborative filtering, and hybrid approaches, while providing practical examples and code snippets for implementation. The author encourages utilizing frameworks like Crab for building recommendation engines, illustrating the process of finding similar users and generating recommendations based on user ratings.
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Construindo Sistemas de Recomendação com Python
- 1. Sistemas de Recomendação usando Python Marcel Pinheiro Caraciolo marcel@pingmind.com @marcelcaraciolo http://www.pycursos.com
- 2. Quem é Marcel? Marcel Pinheiro Caraciolo - @marcelcaraciolo Sergipano, porém Recifense. Mestre em Ciência da Computação no CIN/UFPE na área de mineração de dados Diretor de Pesquisa e Desenvolvimento no Atépassar CEO e Co-fundador do PyCursos/ Pingmind Membro e Moderador da Celúla de Usuários Python de Pernambuco (PUG-PE) Minhas áreas de interesse: Computação móvel e Computação inteligente Meus blogs: http://www.mobideia.com (sobre Mobilidade desde 2006) http://aimotion.blogspot.com (sobre I.A. desde 2009)
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- 5. 1.0 2.0 Fonte de Informação Fluxo Contínuo de Informação VI Encontro do PUG-PE VI Encontro do PUG-PE
- 6. WEB SITES WEB APPLICATIONS WEB SERVICES 3.0 SEMANTIC WEB USERS VI Encontro do PUG-PE VI Encontro do PUG-PE
- 7. Usar informação coletivade forma efetiva afim de aprimorar uma aplicação
- 8. Intelligence from Mining Data User User User User User Um usuário influencia outros por resenhas, notas, recomendações e blogs Um usuário é influenciado por outros por resenhas, notas, recomendações e blogs
- 9. aggregation information: lists ratings user-generated content reviews blogs recommendations wikis Collective Intelligence voting Your application bookmarking Search tag cloud tagging saving Natural Language Processing Clustering and Harness external content predictive models
- 10. WEB SITES WEB APPLICATIONS WEB SERVICES 3.0 SEMANTIC WEB USERS antes... VI Encontro do PUG-PE VI Encontro do PUG-PE
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- 12. estamos sobrecarregados de informações
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- 14. às vezes procuramos isso...
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- 18. eeeeuuuu... google? midias sociais?
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- 20. “A lot oftimes, people don’t know what they want until you show it to them.” Steve Jobs “We are leaving the Information age, and entering into the Recommendation age.” Chris Anderson, from book Long Tail
- 21. Recomendações Sociais Família/Amigos Amigos/ Família O Que eu deveria ler ? Ref: Flickr-BlueAlgae “Eu acho que você deveria ler Ref: Flickr photostream: jefield estes livros.
- 22. Recomendações por Interação Entrada: Avalie alguns livros O Que eu deveria ler ? Saída: “Livros que você pode gostar são …”
- 23. Sistemas desenhados parasugerir algo para mim do meu interesse!
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- 25. Netflix - 2/3dos filmes alugados vêm de recomendação Google News - 38% das notícias mais clicadas vêm de recomendação Amazon - 38% das vendas vêm de recomendação Fonte: Celma & Lamere, ISMIR 2007
- 26. !"#$%"#&'"%(&$)") Nós+,&-.$/).#&0#/"1.#$%234(".# * estamos sobrecarregados de informação $/)#5(&6 7&.2.#"$4,#)$8 * 93((3&/.#&0#:&'3".;#5&&<.# $/)#:-.34#2%$4<.#&/(3/" Milhares de artigos e posts * =/#>$/&3;#?#@A#+B#4,$//"(.;# novos todos os dias 2,&-.$/).#&0#7%&6%$:.# "$4,#)$8 * =/#C"1#D&%<;#."'"%$(# Milhões de Músicas, Filmes e 2,&-.$/).#&0#$)#:"..$6".# Livros ."/2#2&#-.#7"%#)$8 Milhares de Ofertas e Promoções
- 27. O que podeser recomendado ? Contatos em Redes Sociais Artigos Produtos Messagens de Propaganda Cursos e-learning Livros Tags Músicas Futuras namoradas Roupas Filmes Restaurantes Programas de Tv Vídeos Papers Opções de Investimento Profissionais Módulos de código
- 28. E como funcionaa recomendação ?
- 29. O que ossistemas de recomendação realmente fazem ? 1. Prediz o quanto você pode gostar de um certo produto ou serviço 2. Sugere um lista de N items ordenada de acordo com seu interese 3. Sugere uma lista de N usuários ordernada para um produto/serviço 4. Explica a você o porque esses items foram recomendados 5. Ajusta a predição e a recomendação baseado em seu feedback e de outros.
- 30. Filtragem baseada porConteúdo Similar Duro de O Vento Toy Armagedon Items Matar Levou Store recomenda gosta Marcel Usuários
- 31. Problemas com filtragempor conteúdo 1. Análise dos dados Restrita - Items e usuários pouco detalhados. Pior em áudio ou imagens 2. Dados Especializados - Uma pessoa que não tem experiência com Sushi não recebe o melhor restaurante de Sushi da cidade 3. Efeito Portfólio - Só porque eu vi 1 filme da Xuxa quando criança, tem que me recomendar todos dela
- 32. Filtragem Colaborativa O Vento Toy Thor Armagedon Items Levou Store gosta recomenda Marcel Rafael Amanda Usuários Similar
- 33. Problemas com filtragemcolaborativa 1. Escabilidade - Amazon com 5M usuários, 50K items, 1.4B avaliações 2. Dados esparsos - Novos usuários e items que não tem histórico 3. Partida Fria - Só avaliei apenas um único livro no Amazon! 4. Popularidade - Todo mundo lê ‘Harry Potter’ 5. Hacking - A pessoa que lê ‘Harry Potter’ lê Kama Sutra
- 34. Filtragem Híbrida Combinação de múltiplos métodos Duro de O Vento Toy Armagedon Items Matar Levou Store Ontologias Dados Símbolicos Marcel Rafael Luciana Usuários
- 35. Como eles são apresentados ? Destaques Mais sobre este artista... Alguem similar a você também gostou disso O mais popular em seu grupo... Já que você escutou esta, você pode querer esta... Lançamentos Escute músicas de artistas similares. Estes dois item vêm juntos..
- 36. Como eles sãoavaliados ? Como sabemos se a recomendação é boa ? Geralmente se divide-se em treinamento/teste (80/20) Críterios utilizados: - Erro de Predição: RMSE - Curva ROC*, rank-utility, F-Measure *http://code.google.com/p/pyplotmining/
- 37. How to builda recommender system with Python ? There is one option... Crab A Python Framework for Building Recommendation Engines https://github.com/python-recsys/crab
- 38. How to builda recommender system with Python ? There is one option... But it’s still in development! Crab A Python Framework for Building Recommendation Engines https://github.com/python-recsys/crab
- 39. But here wewill create one from Zero with Python! Find someone similar to you O Vento Toy Thor Armagedon Items Levou Store like recommends Marcel Rafael Amanda Users Similar
- 40. But here wewill create one from Step Zero with Python! Find someone similar to you Movies Ratings Dataset
- 41. But here wewill create one from Step Zero with Python! Find someone similar to you Movies Ratings Dataset Mr. X deu nota 4 para Snow Crash e 2 para Girl with the Dragon Tatoo, O que recomendar para ele ?
- 42. But here wewill create one from Step Zero with Python! Find someone similar to you
- 43. But here wewill create one from Step Zero with Python! Find someone similar to you Descobrimos que Amy é mais similar dentre as opções, Podemos recomendar um filme visto por ela com 5 estrelas :)
- 44. But here wewill create one from Step Zero with Python! Mais uma métrica de similaridade: Distância Euclideana
- 45. But here wewill create one from Step Zero with Python! Mais uma métrica de similaridade: Distância Euclideana
- 46. But here wewill create one from Step Zero with Python! Mais uma métrica de similaridade: Distância Euclideana
- 47. Show me thecode!
- 48. Show me thecode! >>>#Representing the data in Python
- 49. Show me thecode! >>>#Representing the data in Python >>>users = {"Angelica": {"Blues Traveler": 3.5, "Broken Bells": 2.0, "Norah Jones": 4.5, "Phoenix": 5.0, "Slightly Stoopid": 1.5, "The Strokes": 2.5, "Vampire Weekend": 2.0}, "Bill": {"Blues Traveler": 2.0, "Broken Bells": 3.5, "Deadmau5": 4.0, "Phoenix": 2.0, "Slightly Stoopid": 3.5, "Vampire Weekend": 3.0}, "Chan": {"Blues Traveler": 5.0, "Broken Bells": 1.0, "Deadmau5": 1.0, "Norah Jones": 3.0, "Phoenix": 5, "Slightly Stoopid": 1.0}, "Dan": {"Blues Traveler": 3.0, "Broken Bells": 4.0, "Deadmau5": 4.5, "Phoenix": 3.0, "Slightly Stoopid": 4.5, "The Strokes": 4.0, "Vampire Weekend": 2.0}, "Hailey": {"Broken Bells": 4.0, "Deadmau5": 1.0, "Norah Jones": 4.0, "The Strokes": 4.0, "Vampire Weekend": 1.0}, "Jordyn": {"Broken Bells": 4.5, "Deadmau5": 4.0, "Norah Jones": 5.0, "Phoenix": 5.0, "Slightly Stoopid": 4.5, "The Strokes": 4.0, "Vampire Weekend": 4.0}, "Sam": {"Blues Traveler": 5.0, "Broken Bells": 2.0, "Norah Jones": 3.0, "Phoenix": 5.0, "Slightly Stoopid": 4.0, "The Strokes": 5.0}, "Veronica": {"Blues Traveler": 3.0, "Norah Jones": 5.0, "Phoenix": 4.0, "Slightly Stoopid": 2.5, "The Strokes": 3.0}}
- 50. Show me thecode!
- 51. Show me thecode! >>>#Representing the data in Python
- 52. Show me thecode! >>>#Representing the data in Python >>>users = {"Angelica": {"Blues Traveler": 3.5, "Broken Bells": 2.0, "Norah Jones": 4.5, "Phoenix": 5.0, "Slightly Stoopid": 1.5, "The Strokes": 2.5, "Vampire Weekend": 2.0}, "Bill": {"Blues Traveler": 2.0, "Broken Bells": 3.5, "Deadmau5": 4.0, "Phoenix": 2.0, "Slightly Stoopid": 3.5, "Vampire Weekend": 3.0}, "Chan": {"Blues Traveler": 5.0, "Broken Bells": 1.0, "Deadmau5": 1.0, "Norah Jones": 3.0, "Phoenix": 5, "Slightly Stoopid": 1.0}, "Dan": {"Blues Traveler": 3.0, "Broken Bells": 4.0, "Deadmau5": 4.5, "Phoenix": 3.0, "Slightly Stoopid": 4.5, "The Strokes": 4.0, "Vampire Weekend": 2.0}, "Hailey": {"Broken Bells": 4.0, "Deadmau5": 1.0, "Norah Jones": 4.0, "The Strokes": 4.0, "Vampire Weekend": 1.0}, "Jordyn": {"Broken Bells": 4.5, "Deadmau5": 4.0, "Norah Jones": 5.0, "Phoenix": 5.0, "Slightly Stoopid": 4.5, "The Strokes": 4.0, "Vampire Weekend": 4.0}, "Sam": {"Blues Traveler": 5.0, "Broken Bells": 2.0, "Norah Jones": 3.0, "Phoenix": 5.0, "Slightly Stoopid": 4.0, "The Strokes": 5.0}, "Veronica": {"Blues Traveler": 3.0, "Norah Jones": 5.0, "Phoenix": 4.0, "Slightly Stoopid": 2.5, "The Strokes": 3.0}}
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- 54. Codificando o Mahantan defmanhattan(rating1, rating2): """Computes the Manhattan distance. Both rating1 and rating2 are dictionaries of the form {'The Strokes': 3.0, 'Slightly Stoopid': 2.5}""" distance = 0 commonRatings = False for key in rating1: if key in rating2: distance += abs(rating1[key] – rating2[key]) commonRatings = True if commonRatings: return distance else: return -1 #Indicates no ratings in common
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- 56. Codificando o Mahantan defmanhattan(rating1, rating2): """Computes the Manhattan distance. Both rating1 and rating2 are dictionaries of the form {'The Strokes': 3.0, 'Slightly Stoopid': 2.5}""" distance = 0 commonRatings = False for key in rating1: if key in rating2: distance += abs(rating1[key] – rating2[key]) commonRatings = True if commonRatings: return distance else: return -1 #Indicates no ratings in common
- 57. Codificando o Mahantan defmanhattan(rating1, rating2): """Computes the Manhattan distance. Both rating1 and rating2 are dictionaries of the form {'The Strokes': 3.0, 'Slightly Stoopid': 2.5}""" distance = 0 commonRatings = False for key in rating1: if key in rating2: distance += abs(rating1[key] – rating2[key]) commonRatings = True if commonRatings: return distance else: return -1 #Indicates no ratings in common >>> manhattan(users['Hailey'], users['Veronica']) 2.0 >>> manhattan(users['Hailey'], users['Jordyn']) 1.5 >>>
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- 59. Codificando Euclidean def euclidean(rating1,rating2): """Computes the euclidean distance. Both rating1 and rating2 are dictionaries of the form {'The Strokes': 3.0, 'Slightly Stoopid': 2.5}""" distance = 0.0 commonRatings = False for key in rating1: if key in rating2: distance += pow(abs(rating1[key] - rating2[key]), 2.0) commonRatings = True if commonRatings: return pow(distance, 1/2.0) else: return -1 #Indicates no ratings in common
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- 61. Codificando Euclidean defeuclidean(rating1, rating2): """Computes the euclidean distance. Both rating1 and rating2 are dictionaries of the form {'The Strokes': 3.0, 'Slightly Stoopid': 2.5}""" distance = 0.0 commonRatings = False for key in rating1: if key in rating2: distance += pow(abs(rating1[key] - rating2[key]), 2.0) commonRatings = True if commonRatings: return pow(distance, 1/2.0) else: return -1 #Indicates no ratings in common 1.4142135623730951
- 62. Codificando Euclidean def euclidean(rating1, rating2): """Computes the euclidean distance. Both rating1 and rating2 are dictionaries of the form {'The Strokes': 3.0, 'Slightly Stoopid': 2.5}""" distance = 0.0 commonRatings = False for key in rating1: if key in rating2: distance += pow(abs(rating1[key] - rating2[key]), 2.0) commonRatings = True if commonRatings: return pow(distance, 1/2.0) else: return -1 #Indicates no ratings in common >>> euclidean(users['Hailey'], users['Veronica']) 1.4142135623730951
- 63. Find the closestusers
- 64. Find the closestusers def computeNearestNeighbor(username, users): """creates a sorted list of users based on their distance to username""" distances = [] for user in users: if user != username: distance = manhattan(users[user], users[username]) distances.append((distance, user)) # sort based on distance -- closest first distances.sort() return distances
- 65. Find the closestusers
- 66. Find the closestusers >>> computeNearestNeighbor('Hailey', users) [(2.0, 'Veronica'), (4.0, 'Chan'),(4.0, 'Sam'), (4.5, 'Dan'), (5.0, 'Angelica'), (5.5, 'Bill'), (7.5, 'Jordyn')] >>>
- 67. Find the closestusers def computeNearestNeighbor(username, users): """creates a sorted list of users based on their distance to username""" distances = [] for user in users: if user != username: distance = manhattan(users[user], users[username]) distances.append((distance, user)) # sort based on distance -- closest first distances.sort() return distances >>> computeNearestNeighbor('Hailey', users) [(2.0, 'Veronica'), (4.0, 'Chan'),(4.0, 'Sam'), (4.5, 'Dan'), (5.0, 'Angelica'), (5.5, 'Bill'), (7.5, 'Jordyn')] >>>
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- 69. The recommender def recommend(username,users): """Give list of recommendations""" # first find nearest neighbor nearest = computeNearestNeighbor(username, users)[0][1] recommendations = [] # now find bands neighbor rated that user didn't neighborRatings = users[nearest] userRatings = users[username] for artist in neighborRatings: if not artist in userRatings: recommendations.append((artist, neighborRatings[artist])) recommendations.sort(key=lambda artistTuple: artistTuple[1], reverse = True) return recommendations
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- 71. The recommender >>> recommend('Hailey',users) [('Phoenix', 4.0), ('Blues Traveler', 3.0), ('Slightly Stoopid', 2.5)] >>> recommend('Chan', users) [('The Strokes', 4.0), ('Vampire Weekend', 1.0)] >>> recommend('Angelica', users) []
- 72. The recommender def recommend(username, users): """Give list of recommendations""" # first find nearest neighbor nearest = computeNearestNeighbor(username, users)[0][1] recommendations = [] # now find bands neighbor rated that user didn't neighborRatings = users[nearest] userRatings = users[username] for artist in neighborRatings: if not artist in userRatings: recommendations.append((artist, neighborRatings[artist])) recommendations.sort(key=lambda artistTuple: artistTuple[1], reverse = True) return recommendations >>> recommend('Hailey', users) [('Phoenix', 4.0), ('Blues Traveler', 3.0), ('Slightly Stoopid', 2.5)] >>> recommend('Chan', users) [('The Strokes', 4.0), ('Vampire Weekend', 1.0)] >>> recommend('Angelica', users) []
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- 74. The recommender >>> computeNearestNeighbor('Angelica',users) [(3.5, 'Veronica'), (4.5, 'Chan'), (5.0, 'Hailey'), (8.0, 'Sam'), (9.0, 'Bill'), (9.0, 'Dan'), (9.5, 'Jordyn')]('Hailey', users)
- 75. The recommender defrecommend(username, users): """Give list of recommendations""" # first find nearest neighbor nearest = computeNearestNeighbor(username, users)[0][1] recommendations = [] # now find bands neighbor rated that user didn't neighborRatings = users[nearest] userRatings = users[username] for artist in neighborRatings: if not artist in userRatings: recommendations.append((artist, neighborRatings[artist])) recommendations.sort(key=lambda artistTuple: artistTuple[1], reverse = True) return recommendations >>> computeNearestNeighbor('Angelica', users) [(3.5, 'Veronica'), (4.5, 'Chan'), (5.0, 'Hailey'), (8.0, 'Sam'), (9.0, 'Bill'), (9.0, 'Dan'), (9.5, 'Jordyn')]('Hailey', users)
- 76. But we needto improve it more...
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- 81. The Pearson Correlation Output:-1 (perfect disagreement) to 1 (perfect agreement)
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- 84. Pearson Correlation def pearson(rating1,rating2): sum_xy = 0 sum_x = 0 sum_y = 0 sum_x2 = 0 sum_y2 = 0 n = 0 for key in rating1: if key in rating2: n += 1 x = rating1[key] y = rating2[key] sum_xy += x * y sum_x += x sum_y += y sum_x2 += x**2 sum_y2 += y**2 # now compute denominator denominator = sqrt(sum_x2 - (sum_x**2) / n) * sqrt(sum_y2 -(sum_y**2) / n) if denominator == 0: return 0 else: return (sum_xy - (sum_x * sum_y) / n) / denominator
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- 86. Pearson Correlation >>> pearson(users['Angelica'],users['Bill']) -0.90405349906826993 >>> pearson(users['Angelica'], users['Hailey']) 0.42008402520840293 >>> pearson(users['Angelica'], users['Jordyn']) 0.76397486054754316 >>>
- 87. Pearson Correlation def pearson(rating1,rating2): sum_xy = 0 sum_x = 0 sum_y = 0 sum_x2 = 0 sum_y2 = 0 n = 0 for key in rating1: if key in rating2: n += 1 x = rating1[key] y = rating2[key] sum_xy += x * y sum_x += x sum_y += y sum_x2 += x**2 sum_y2 += y**2 # now compute denominator denominator = sqrt(sum_x2 - (sum_x**2) / n) * sqrt(sum_y2 -(sum_y**2) / n) if denominator == 0: return 0 else: return (sum_xy - (sum_x * sum_y) / n) / denominator >>> pearson(users['Angelica'], users['Bill']) -0.90405349906826993 >>> pearson(users['Angelica'], users['Hailey']) 0.42008402520840293 >>> pearson(users['Angelica'], users['Jordyn']) 0.76397486054754316 >>>
- 88. Which one tochoose ?
- 89. K-nearest Neighbors (kNN) Findk most similars to you
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- 93. Change people toitems to
- 94. Change people toitems {'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5}, 'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5}} to {'Lady in the Water':{'Lisa Rose':2.5,'Gene Seymour':3.0}, 'Snakes on a Plane':{'Lisa Rose':3.5,'Gene Seymour':3.5}} etc.
- 95. Change people toitems to
- 96. Change people toitems {'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5}, 'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5}} to {'Lady in the Water':{'Lisa Rose':2.5,'Gene Seymour':3.0}, 'Snakes on a Plane':{'Lisa Rose':3.5,'Gene Seymour':3.5}} etc.
- 97. Change people toitems {'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5}, 'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5}} to {'Lady in the Water':{'Lisa Rose':2.5,'Gene Seymour':3.0}, 'Snakes on a Plane':{'Lisa Rose':3.5,'Gene Seymour':3.5}} etc. def transformPrefs(prefs): result={} for person in prefs: for item in prefs[person]: result.setdefault(item,{}) # Flip item and person result[item][person]=prefs[person][item] return result
- 98. Change people toitems {'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5}, 'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5}} to {'Lady in the Water':{'Lisa Rose':2.5,'Gene Seymour':3.0}, 'Snakes on a Plane':{'Lisa Rose':3.5,'Gene Seymour':3.5}} etc. def transformPrefs(prefs): result={} for person in prefs: for item in prefs[person]: result.setdefault(item,{}) # Flip item and person result[item][person]=prefs[person][item] return result >> movies=recommendations.transformPrefs(recommendations.users) >> recommendations.computeNearestNeighbors(‘Blues Traveler’, movies) [(0.657, 'You, Me and Dupree'), (0.487, 'Lady in the Water'), (0.111, 'Snakes on a Plane'), (-0.179, 'The Night Listener'), (-0.422, 'Just My Luck')]
- 99. Change people toitems to
- 100. Change people toitems {'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5}, 'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5}} to {'Lady in the Water':{'Lisa Rose':2.5,'Gene Seymour':3.0}, 'Snakes on a Plane':{'Lisa Rose':3.5,'Gene Seymour':3.5}} etc.
- 101. Change people toitems {'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5}, 'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5}} to {'Lady in the Water':{'Lisa Rose':2.5,'Gene Seymour':3.0}, 'Snakes on a Plane':{'Lisa Rose':3.5,'Gene Seymour':3.5}} etc. def transformPrefs(prefs): result={} for person in prefs: for item in prefs[person]: result.setdefault(item,{}) # Flip item and person result[item][person]=prefs[person][item] return result
- 102. Change people toitems {'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5}, 'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5}} to {'Lady in the Water':{'Lisa Rose':2.5,'Gene Seymour':3.0}, 'Snakes on a Plane':{'Lisa Rose':3.5,'Gene Seymour':3.5}} etc. def transformPrefs(prefs): result={} for person in prefs: for item in prefs[person]: result.setdefault(item,{}) # Flip item and person result[item][person]=prefs[person][item] return result >> movies=recommendations.transformPrefs(recommendations.critics) >> recommendations.computeNearestNeighbors(movies,'Superman Returns') [(0.657, 'You, Me and Dupree'), (0.487, 'Lady in the Water'), (0.111, 'Snakes on a Plane'), (-0.179, 'The Night Listener'), (-0.422, 'Just My Luck')]
- 103. User Based Filteringaté agora! Problemas de Escalabilidade e Esparsidade
- 104. Item Based Filtering Findk most similars to the item
- 105. Find the closestitems
- 106. Find the closestitems def calculateSimilarItems(prefs,sim_distance=manhattan): ! # Create a dictionary of items showing which other items they ! # are most similar to. ! result={} ! # Invert the preference matrix to be item-centric ! itemPrefs=transformPrefs(prefs) ! c=0 ! for item in itemPrefs: ! ! # Status updates for large datasets ! ! c+=1 ! ! if c%100==0: print "%d / %d" % (c,len(itemPrefs)) # Find the most similar items to this one scores=computeNearestNeighbor(item,itemPrefs,distance=sim_distance) result[item]=scores ! return result
- 107. Find the closestitems def calculateSimilarItems(prefs,sim_distance=manhattan): ! # Create a dictionary of items showing which other items they ! # are most similar to. ! result={} ! # Invert the preference matrix to be item-centric ! itemPrefs=transformPrefs(prefs) ! c=0 ! for item in itemPrefs: ! ! # Status updates for large datasets ! ! c+=1 ! ! if c%100==0: print "%d / %d" % (c,len(itemPrefs)) # Find the most similar items to this one scores=computeNearestNeighbor(item,itemPrefs,distance=sim_distance) result[item]=scores ! return result >>> itemsim=recommendations.calculateSimilarItems(users) >>> itemsim {'Lady in the Water': [(0.40000000000000002, 'You, Me and Dupree'), (0.2857142857142857, 'The Night Listener'),... 'Snakes on a Plane': [(0.22222222222222221, 'Lady in the Water'), (0.18181818181818182, 'The Night Listener'),... etc.
- 108.
- 109. The recommender def recommend(username,users,similarities, n=3): scores = {} totalSim = {} # # now get the ratings for the user # userRatings = users[username] # Loop over items rated by this user for item, rating in userRatings.items(): #Loop over items similar to this one for sim, other_item in similarities[item]: # Ignore if this user has already rated this item if other_item in userRatings: continue # Weighted sum of rating times similarity scores.setdefault(other_item, 0.0) scores[other_item]+= sim * rating # Sum of all the similarities totalSim.setdefault(other_item, 0.0) totalSim[other_item] += sim # Divide each total score by total weighting to get an average recommendations = [(score/totalSim[item],item) for item,score in scores.items()] # finally sort and return recommendations.sort(key=lambda artistTuple: artistTuple[1], reverse = True) # Return the first n items return recommendations[:n]
- 110. The recommender >>> recommend('Hailey',users,similarities,3) [(3.1176470588235294, 'Slightly Stoopid'), (2.639207507820647, 'Phoenix'),(2.64476386036961, 'Blues Traveler')]
- 111. The recommender def recommend(username,users, similarities, n=3): scores = {} totalSim = {} # # now get the ratings for the user # userRatings = users[username] # Loop over items rated by this user for item, rating in userRatings.items(): #Loop over items similar to this one for sim, other_item in similarities[item]: # Ignore if this user has already rated this item if other_item in userRatings: continue # Weighted sum of rating times similarity scores.setdefault(other_item, 0.0) scores[other_item]+= sim * rating # Sum of all the similarities totalSim.setdefault(other_item, 0.0) totalSim[other_item] += sim # Divide each total score by total weighting to get an average recommendations = [(score/totalSim[item],item) for item,score in scores.items()] # finally sort and return recommendations.sort(key=lambda artistTuple: artistTuple[1], reverse = True) # Return the first n items return recommendations[:n] >>> recommend('Hailey', users,similarities,3) [(3.1176470588235294, 'Slightly Stoopid'), (2.639207507820647, 'Phoenix'),(2.64476386036961, 'Blues Traveler')]
- 112. Content Based Filtering Similar Duro de O Vento Toy Armagedon Items Matar Levou Store recommend likes Marcel Users
- 113. source, the recommendationarchitecture that we propose will would rely more on collaborative-filtering techniques, that is, aggregate the results of such filtering techniques. Bezerra and Carvalho proposed approaches where the results the reviews from similar users. We aim at integrating the previously mentioned hybrid prod- Figure 1 shows a overview of our meta recommender achieved showed to be very promising [19]. approach. By combining the content-based filtering and the uct recommendation approach in a mobile application so the A. Crab is already in production users could benefit from useful and logical recommendations. collaborative-based one into a hybrid recommender system, it Moreover, we aim at providing a suited explanation for each would use the services/products III. S YSTEM catalogues repositories which D ESIGN recommendation to the user, since the current approaches just the services to be recommended, and the review repository Application data information our mobile recommender sys- that contains the user opinions about those services. All this for only deliver product recommendations with a overall score without pointing out the appropriateness of such recommen- datatembecan be from data source containers in the web product description can extracted divided into two parts: the rec dation [13]. Besides the basic information provided by the such(such location-based social network Foursquare its attributes) and the user as the as location, description and [17] as Hybrid Meta Approach gives the system’s architecture and suppliers, the system will deliver the explanation, providing relevant reviews of similar users, we believe that it will tags, etc.). The Figure 3 increase the confidence in the buying decision process and the displayed at the Figure 2 and the location recommendation engine from Google: Google HotPot [18]. by user (such as rating, comments, reviews or ratings provided mo wh product accepptance rate. In the mobile context this approach po could help the users in this process and showing the user relative components. thi opinions could contribute to achieve this task. rec spe !"#$"%&'$ 5&-$ !"#$%&'%($) !".,"/#) acc !"*+#,$+'-) !"*+#,$+'-) +,-*.&$ !(#$()&'*&%$ /01&'234&$ !6#$6,00&41&7$ wh res !<#$<'&2&'&04&%A$B,431*,0A$&14C$ ves 0+44%6+'%$,.")1%#"2) 0+($"($)1%#"2) 3,4$"',(5) ou 3,4$"',(5) )))67,8,#%)+,4%$91$'%4)-1":)))) suc !"#$%&"'()*+,#&-,.) /$%,0"12()*3$4%)3""5.) ))))1,;&,<4)<1&%%,')=2)4&:&8$1)) )))))))))))%$4%,5)94,14>?) <',7)41$ pro 8&=,%*1,'>$ exp 8&4,99&0731*,0$:0;*0&$ !B#$B*%1$,2$D4,'&7$<',7)41%$ !(#$()&'*&%$ ma 8&?*&@$ we Fig. 2. User Reviews from Foursquare Social Network 8&=,%*1,'>$ com 7"$%) !"8+99"(2"')) !8#$830E&7$<',7)41%$ The content-based filtering approach will be used to filter ext the product/service repository, while the collaborative based 8&%).1%$ B. approach will derive the product review recommendations. In addition we will use text mining techniques to distinct the !"8+99"(2%$,+(#) polarity of the user review between positive or negative one. This information summarized would contribute in the product Architecture Fig. 3. Mobile Recommender System rat score recommendation computation. The final product recom- Fig. 1. Meta Recommender Architecture mendation score is computed by integrating the result of both me recommenders. By now, weproduct/service recommender, the user could In our mobile are considering to use different and Since one of the goals of this work is to incorporate options regarding this integration approach, one and get a list of recommen- different data sources of user opinions and descriptions, we filter some products or services at special oth is the symbolic data analysis approach (SDA) [19], which have addopted an meta recommendation architecture. By using eachtations. The user user ratings/reviews arehis preferences or give his product description and also can enter modeled ow a meta recommender architecture, the system would provide a personalized control over the generated recommendation list feedback to some offered product recommendation. as set of modal symbolic descriptions that summarizes the Re information provided by the corresponding data sources. It is
- 114. Crab is alreadyin production Brazilian Social Network called Atepassar.com Educational network with more than 60.000 students and 120 video-classes Running on Python + Numpy + Scipy and Django Backend for Recommendations MongoDB - mongoengine Daily Recommendations with Explanations
- 115. Distributing the recommendationcomputations Use Hadoop and Map-Reduce intensively Investigating the Yelp mrjob framework https://github.com/pfig/mrjob Develop the Netflix and novel standard-of-the-art used Matrix Factorization, Singular Value Decomposition (SVD), Boltzman machines The most commonly used is Slope One technique. Simple algebra math with slope one algebra y = a*x+b
- 116. Distributed Computing withmrJob https://github.com/Yelp/mrjob http://aimotion.blogspot.com/2012/08/introduction-to-recommendations-with.html
- 117. Distributed Computing withmrJob https://github.com/Yelp/mrjob It supports Amazon’s Elastic MapReduce(EMR) service, your own Hadoop cluster or local (for testing) http://aimotion.blogspot.com/2012/08/introduction-to-recommendations-with.html
- 118. Distributed Computing withmrJob https://github.com/Yelp/mrjob It supports Amazon’s Elastic MapReduce(EMR) service, your own Hadoop cluster or local (for testing) http://aimotion.blogspot.com/2012/08/introduction-to-recommendations-with.html
- 119. Distributed Computing withmrJob https://github.com/Yelp/mrjob """The classic MapReduce job: count the frequency of words. """ from mrjob.job import MRJob import re WORD_RE = re.compile(r"[w']+") class MRWordFreqCount(MRJob): def mapper(self, _, line): for word in WORD_RE.findall(line): yield (word.lower(), 1) def reducer(self, word, counts): yield (word, sum(counts)) if __name__ == '__main__': MRWordFreqCount.run() It supports Amazon’s Elastic MapReduce(EMR) service, your own Hadoop cluster or local (for testing) http://aimotion.blogspot.com/2012/08/introduction-to-recommendations-with.html
- 120. Future studies withSparse Matrices Real datasets come with lots of empty values http://aimotion.blogspot.com/2011/05/evaluating-recommender-systems.html Solutions: scipy.sparse package Sharding operations Matrix Factorization techniques (SVD) Apontador Reviews Dataset
- 121. Future studies withSparse Matrices Real datasets come with lots of empty values http://aimotion.blogspot.com/2011/05/evaluating-recommender-systems.html Solutions: scipy.sparse package Sharding operations Matrix Factorization techniques (SVD) Crab implements a Matrix Factorization with Expectation Maximization algorithm Apontador Reviews Dataset
- 122. Future studies withSparse Matrices Real datasets come with lots of empty values http://aimotion.blogspot.com/2011/05/evaluating-recommender-systems.html Solutions: scipy.sparse package Sharding operations Matrix Factorization techniques (SVD) Crab implements a Matrix Factorization with Expectation Maximization algorithm scikits.crab.svd package Apontador Reviews Dataset
- 123. How are weworking ? Our Project’s Home Page http://github.com/python-recsys/crab
- 124. Future Releases Planned Release 0.1 Collaborative Filtering Algorithms working, sample datasets to load and test Planned Release 0.11 Sparse Matrixes and Database Models support Planned Release 0.12 Slope One Agorithm, new factorization techniques implemented ....
- 125. Join us! 1. Readour Wiki Page https://github.com/python-recsys/crab/wiki/Developer-Resources 2. Check out our current sprints and open issues https://github.com/python-recsys/crab/issues 3. Forks, Pull Requests mandatory 4. Join us at irc.freenode.net #muricoca or at our discussion list http://groups.google.com/group/scikit-crab
- 126. Construção do Social Genoma
- 128.
- 129. Recommended Books Toby Segaran,Programming Collective SatnamAlag, Collective Intelligence in Intelligence, O'Reilly, 2007 Action, Manning Publications, 2009 ACM RecSys, KDD , SBSC...
- 130. Conferências Recomendadas - ACMRecSys. –ICWSM: Weblogand Social Media –WebKDD: Web Knowledge Discovery and Data Mining –WWW: The original WWW conference –SIGIR: Information Retrieval –ACM KDD: Knowledge Discovery and Data Mining –ICML: Machine Learning
- 131. Sistemas de Recomendação usando Python Marcel Pinheiro Caraciolo marcel@pingmind.com @marcelcaraciolo http://www.pycursos.com