Sistemas de Recomendação e Mobilidade

Sistemas de
Recomendação

Marcel Pinheiro Caraciolo
mpc@cin.ufpe.br / marcel@orygens.com/marcel@recday.com

@marcelcaraciolo

Thursday, September 22, 2011

Quem é Marcel ?
Marcel Pinheiro Caraciolo - @marcelcaraciolo

Sergipano, porém Recifense.
Mestrando em Ciência da Computação no CIN/UFPE na área de mineração de dados
Diretor de Pesquisa e Desenvolvimento na Orygens
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)

Jovem Aprendiz ainda nas artes pythonicas.... (desde 2007)


WEB


1.0 2.0

Fonte de Informação Fluxo Contínuo de Informação
VI Encontro do PUG-PE


WEB SITES
WEB APPLICATIONS
WEB SERVICES
3.0 SEMANTIC WEB

USERS VI Encontro do PUG-PE


Usar informação coletiva de
forma efetiva afim de
aprimorar uma aplicação


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


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


WEB SITES
WEB APPLICATIONS
WEB SERVICES
3.0 SEMANTIC WEB

USERS
antes...

Friday, October 1, 2010 2011
Thursday, September 22,

Atualmente


estamos sobrecarregados
de informações


muitas vezes inúteis

Friday, October 1, 2010

às vezes
procuramos
isso...

Friday, October 1, 201022, 2011
Thursday, September

e encontramos isso!


google?

Friday, October 1, 201022, 2011
Thursday, September

google?

midias sociais?


eeeeuuuu...

google?

midias sociais?

riday, October 1, 2010 22, 2011
Thursday, September

Sistemas de Recomendação

“A lot of times, 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


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.


Recomendações por Interação

Entrada: Avalie alguns livros

O Que eu
deveria ler ?

Saída:
“Livros que você
pode gostar
são …”


Sistemas desenhados para sugerir algo para mim do meu
interesse!


Por que Recomendação ?


Netﬂix
- 2/3 dos 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


!"#$%"#&'"%(&$)")
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


O que pode ser 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


E como funciona a
recomendação ?


O que os sistemas 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.

Filtragem baseada por Conteúdo

Similar

Duro de O Vento Toy
Armagedon Items
Matar Levou Store

recomenda
gosta

Marcel Usuários


Problemas com filtragem por
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


Filtragem Colaborativa

O Vento Toy
Thor Armagedon Items
Levou Store

gosta
recomenda

Marcel Rafael Amanda Usuários

Similar


Problemas com filtragem colaborativa
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

Filtragem Híbrida
Combinação de múltiplos métodos

Duro de O Vento Toy
Armagedon Items
Matar Levou Store

Ontologias
Dados
Símbolicos

Marcel Rafael Luciana Usuários


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..


Como eles são avaliados ?
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/


Mobile Recommenders


Por que mobile ?

Mais de 1 bilhão de Aparelhos
Mais de 5 bilhões de apps baixadas

Destaque no segmento mobile
http://foursquare.com

http://vimeo.com/29323612

Sistemas de Recomendação Móvel

Deve-se levar em conta informações temporais e espaciais

Como deﬁnir que contexto ele está inserido ?

E as avaliações como ser capturadas em uma tela limitada?


a strong heterogeneity. At case study is carried out in Section 5. Finaly, the
ser's location is constantly conclusion of this paper and future work
ata-processing capability in overview are discussed in Section 6.
WSEAS TRANSACTIONS on COMPUTERS
services on the system
ht new challenges [4-6].
type of location-based
approach, users want to be
e real-time and targeted
2 System Workflow and Architecture
Arquitetura
Figure 1 gives the workflow of our system.
repackage the heterogeneous data and service,
and republic them as web service. The
service com
new code to
not just the indexed Users can send their inquiries demand by successful design of this module is the key After an
simply on a static operating in the mobile phone. And the client problem for realization of cross-platform new appl
mechanism
tly, the rise of a large
.0 applications (blog, Recomendações processadas via Mobile (Inviável Hoje)
will get the current location information and sent
it together with users’ inqueries demand to the
service and data sharing.
The functional layer has three components as
Multi-Mode Location Information Index,
service m
large-scal
Web Albums, Blog and server. Server-side application will analyze the Thus it ca
tes that users have the very relevant data and provide matched restaurant Context-based Collaborative Filtering changing
of direct, rapid, useful and recommendation and navigation. Algorithm, and Location-based Personalized So in th
tion recommendation and - Tudo é processado em Back-End (Servidor)
Application data information of our system e enviado ao celular via Web
Recommendation and Navigation. We will and Serv
]. can be divided into two parts: the location-based discuss every function component in details as Middlewa
n can be user-friendly data (such as traffic and road condition data, follows. Architectu
GPS map, and entity information, etc.) and the two techn
ient mobile terminals, It
Value-added Services integration
a very important research value-added data provided by users (such as combinati
in Web 2.0
very wide market prospect. Ratings, Comments, Blog and Tags, etc.). User Tagging !!Despite th
Value-added DB
signs and realizes a Comments Tags Information Publish platforms h
User
mobile restaurant Ratings …..…. Recommendation information
navigation system. In order Restaurant Query
……... Ping” webs
side response speed for facilities ra
propose a memory pool Location-based DB
website, wh
Client However, it
Accept command, no-data GPS-info E-Map
Entity-info ……... Mobile Information Pushing Platform static guidin
terrupt mechanism, which Prescribed Location-based Info. mobile loca
Context-based Location-based
ize the server-side control Users‘ Collaborative Filtering inconvenien
personalized
ient side, we combine the Matched Entity Collaborative recommendation and with the visi
lication data with the & Route Info. Recommendation &
Multi-Mode Location
Navigation In order
Entity Feature Info. scenario as
nd propose a collaborative Information Index
mmend mechanisms, which and propose
Server
h real-time location-based Let us
Personalized Location-based Data and Service Middleware example.
ecommend personalized Location-based Value-added DB location a
Restaurant Comments Tags
Recommendation &
from its c
ually provide personalized Ratings …..…. through th
Navigation Services
ndation to build their own Clien informatio
Location-based
h can help them to consider Services informatio
munity users!collaborative Fig.1. System Workflow Location-based GPS Navigation current lo
DB informatio
Location-based info
Traffic-info
Booking the targe
E-Map
Entity-query informatio
matching
810 Issue 5, Volume 6, May 2009
informatio
Fig 1. Architecture of the Mobile Information
Thursday, September 22, 2011 Accordi

Informações Disponíveis

Localização, Tags, Contexto


Informações Disponíveis

Avaliação
Implícita


Um dos mais populares
sistemas de localização móvel

Checkins, diga aonde você está!

Recomendações de lugares


Assistente Virtual Móvel Conversacional
Já se utiliza de informações das redes Sociais
Recomendação de Restaurantes


Google HotPot

Repositório de Reviews
Recomendação de Lugares


Minhas contribuições


Meu trabalho de Mestrado

Offering Products and Services Using Product
Reviews from Social Networks in Mobile Decision
Aid Systems
Marcel Caraciolo∗ and Germano Vasconcelos†
Informatics Center
Federal University Of Pernambuco
WebSite: http://www.cin.ufpe.br/
Email: ∗ mpc@cin.ufpe.br
† gcv@cin.ufpe.br

Abstract—Recommendation engines provide information fil- extremely used by users to give a more nuanced view about
tering functions and decision aids that have a great potential a product in order to make an informed decision [5].
application the mobile context. An aspect that hasn’t been Nonetheless, providing users with relevant recommenda-
extensively exploited yet in the current recommendations is
the improvement in the explanation of the recommendation. tion information it is a difficult task. Besides the technical
For instance, exploiting the service and product description components such as the user model representation and infor-
and the opinion of users about the recommended products, mation filtering techniques to generate the recommendations,
where associated would bring a better explanation for the user. the information must be user-friendly visualized. This is a
In this paper we will present the foundations for a mobile requirement specially to support the user in the purchase
product/service recommender system which incorporate both
Thursday, September 22, 2011structured (supplier driven) product descriptions and subject decision process, and to convince him about the utility of the

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
users could benefit from useful and logical recommendations. collaborative-based one into a hybrid recommender system, it A.
Moreover, we aim at providing a suited explanation for each would use the services/products III. S YSTEM catalogues
repositories which D ESIGN

How reviews from web services sources can be aggregated in the for
recommendation to the user, since the current approaches just
only deliver product recommendations with a overall score
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
datatembecan be from data source containers in the web product description
can extracted divided into two parts: the
rec
mobile recommendation process?
without pointing out the appropriateness of such recommen-
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
mo
suppliers, the system will deliver the explanation, providing displayed at the Figure 2 and the location recommendation
relevant reviews of similar users, we believe that it will engine from Google: Google HotPot [18]. by user (such as rating, comments,
reviews or ratings provided wh
increase the confidence in the buying decision process and the tags, etc.). The Figure 3 gives the system’s architecture and po
product accepptance rate. In the mobile context this approach
could help the users in this process and showing the user
relative components. thi
opinions could contribute to achieve this task. rec
spe
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Fig. 2. User Reviews from Foursquare Social Network 8&=,%*1,'>$
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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
formed by the combination of rich data [16]. The influence
Thursday, September 22, 2011 a novel Other functionalities are systems which,i n of the next ve best
approach in hybrid recommender the retrieval the
of the specific data sources could be explicitly controlled by

Text Mining A Lot!

Sentiment Analysis for Extracting the Polarity
Meta-Recommender Engines
Content-Based Filtering
kNN - Nearest Neighbors
Hybrid Meta Recommender
Symbolic Data Analysis (SDA)

Evaluation in Experimental DataSets

Architectural Proposal for Mobile Recommender

Crab
A Python Framework for Building
Recommendation Engines

Marcel Caraciolo Ricardo Caspirro Bruno Melo
@marcelcaraciolo @ricardocaspirro @brunomelo


What is Crab ?

A python framework for building recommendation engines
A Scikit module for collaborative, content and hybrid ﬁltering
Mahout Alternative for Python Developers :D
Open-Source under the BSD license

https://github.com/muricoca/crab


The current Crab

Collaborative Filtering algorithms
User-Based, Item-Based and Slope One

Evaluation of the Recommender Algorithms
Precision, Recall, F1-Score, RMSE

Precision-Recall Charts


Why migrate ?
Old Crab running only using Pure Python
Recommendations demand heavy maths calculations and lots of processing

Compatible with Numpy and Scipy libraries
High Standard and popular scientific libraries optimized for scientific calculations in Python

Scikits projects are amazing!
Active Communities, Scientific Conferences and updated projects (e.g. scikit-learn)

Turn the Crab framework visible for the community
Join the scientific researchers and machine learning developers around the Globe coding with
Python to help us in this project

Be Fast and Furious


How are we working ?
Sprints, Online Discussions and Issues

https://github.com/muricoca/crab/wiki/UpcomingEvents


Future Releases
Planned Release 0.1
Collaborative Filtering Algorithms working, sample datasets to load and test

Planned Release 0.11
Evaluation of Recommendation Algorithms and Database Models support

Planned Release 0.12
Recommendation as Services with REST APIs

....


Join us!

1. Read our Wiki Page
https://github.com/muricoca/crab/wiki/Developer-Resources

2. Check out our current sprints and open issues
https://github.com/muricoca/crab/issues

3. Forks, Pull Requests mandatory

4. Join us at irc.freenode.net #muricoca or at our
discussion list in work :(


RecDay: Recomendações diariamente!


Dicas


Items Recomendados

Toby Segaran, Programming Collective SatnamAlag, Collective Intelligence in
Intelligence, O'Reilly, 2007 Action, Manning Publications, 2009

Sites como TechCrunch e ReadWriteWeb


Conferências Recomendadas
- ACM RecSys.

–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


Onde você estará em tudo
isso ?

Fonte: Hunch.com

Obrigado !!


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