Sistemas de Recomendação e Mobilidade
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Apresentação Realizada na Disciplina de Redes Neurais no CIN/UFPE em 22.09.2011

Apresentação Realizada na Disciplina de Redes Neurais no CIN/UFPE em 22.09.2011

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Sistemas de Recomendação e Mobilidade Presentation Transcript

  • 1. Sistemas de Recomendação Marcel Pinheiro Caraciolo mpc@cin.ufpe.br / marcel@orygens.com/marcel@recday.com @marcelcaracioloThursday, September 22, 2011
  • 2. 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)Thursday, September 22, 2011
  • 3. WEBThursday, September 22, 2011
  • 4. WEBThursday, September 22, 2011
  • 5. 1.0 2.0 Fonte de Informação Fluxo Contínuo de Informação VI Encontro do PUG-PE VI Encontro do PUG-PEThursday, September 22, 2011
  • 6. WEB SITES WEB APPLICATIONS WEB SERVICES 3.0 SEMANTIC WEB USERS VI Encontro do PUG-PE VI Encontro do PUG-PEThursday, September 22, 2011
  • 7. Usar informação coletiva de forma efetiva afim de aprimorar uma aplicaçãoThursday, September 22, 2011
  • 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 blogsThursday, September 22, 2011
  • 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 modelsThursday, September 22, 2011
  • 10. WEB SITES WEB APPLICATIONS WEB SERVICES 3.0 SEMANTIC WEB USERS antes... VI Encontro do PUG-PE VI Encontro do PUG-PEFriday, October 1, 2010 2011 Thursday, September 22,
  • 11. AtualmenteThursday, September 22, 2011
  • 12. estamos sobrecarregados de informaçõesThursday, September 22, 2011
  • 13. muitas vezes inúteis Thursday, September 22, 2011Friday, October 1, 2010
  • 14. às vezes procuramos isso...Friday, October 1, 201022, 2011 Thursday, September
  • 15. e encontramos isso!Friday, October 1, 2010 2011 Thursday, September 22,
  • 16. google?Friday, October 1, 201022, 2011 Thursday, September
  • 17. google? midias sociais?Friday, October 1, 2010 2011Thursday, September 22,
  • 18. eeeeuuuu... google? midias sociais?riday, October 1, 2010 22, 2011 Thursday, September
  • 19. Sistemas de RecomendaçãoThursday, September 22, 2011
  • 20. “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 TailThursday, September 22, 2011
  • 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.Thursday, September 22, 2011
  • 22. Recomendações por Interação Entrada: Avalie alguns livros O Que eu deveria ler ? Saída: “Livros que você pode gostar são …”Thursday, September 22, 2011
  • 23. Sistemas desenhados para sugerir algo para mim do meu interesse!Thursday, September 22, 2011
  • 24. Por que Recomendação ?Thursday, September 22, 2011
  • 25. 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 2007Thursday, September 22, 2011
  • 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 PromoçõesThursday, September 22, 2011
  • 27. 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 códigoThursday, September 22, 2011
  • 28. E como funciona a recomendação ?Thursday, September 22, 2011
  • 29. 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.Thursday, September 22, 2011
  • 30. Filtragem baseada por Conteúdo Similar Duro de O Vento Toy Armagedon Items Matar Levou Store recomenda gosta Marcel UsuáriosThursday, September 22, 2011
  • 31. 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 delaThursday, September 22, 2011
  • 32. Filtragem Colaborativa O Vento Toy Thor Armagedon Items Levou Store gosta recomenda Marcel Rafael Amanda Usuários SimilarThursday, September 22, 2011
  • 33. 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 SutraThursday, September 22, 2011
  • 34. Filtragem Híbrida Combinação de múltiplos métodos Duro de O Vento Toy Armagedon Items Matar Levou Store Ontologias Dados Símbolicos Marcel Rafael Luciana UsuáriosThursday, September 22, 2011
  • 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..Thursday, September 22, 2011
  • 36. 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/Thursday, September 22, 2011
  • 37. Mobile RecommendersThursday, September 22, 2011
  • 38. 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/29323612Thursday, September 22, 2011
  • 39. 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?Thursday, September 22, 2011
  • 40. a strong heterogeneity. At case study is carried out in Section 5. Finaly, thesers location is constantly conclusion of this paper and future workata-processing capability in overview are discussed in Section 6. WSEAS TRANSACTIONS on COMPUTERS services on the systemht 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 DBsigns and realizes a Comments Tags Information Publish platforms h User mobile restaurant Ratings …..…. Recommendation informationnavigation system. In order Restaurant Query ……... Ping” websside 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 Indexmmend 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-basedh can help them to consider Services informatiomunity 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
  • 41. Informações Disponíveis Localização, Tags, ContextoThursday, September 22, 2011
  • 42. Informações Disponíveis Avaliação ImplícitaThursday, September 22, 2011
  • 43. Um dos mais populares sistemas de localização móvel Checkins, diga aonde você está! Recomendações de lugaresThursday, September 22, 2011
  • 44. Assistente Virtual Móvel Conversacional Já se utiliza de informações das redes Sociais Recomendação de RestaurantesThursday, September 22, 2011
  • 45. Google HotPot Repositório de Reviews Recomendação de LugaresThursday, September 22, 2011
  • 46. Minhas contribuiçõesThursday, September 22, 2011
  • 47. 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 bothThursday, September 22, 2011structured (supplier driven) product descriptions and subject decision process, and to convince him about the utility of the
  • 48. 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 !"#$"%&$ 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 formed by the combination of rich data [16]. The influenceThursday, 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
  • 49. 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 RecommenderThursday, September 22, 2011
  • 50. Crab A Python Framework for Building Recommendation Engines Marcel Caraciolo Ricardo Caspirro Bruno Melo @marcelcaraciolo @ricardocaspirro @brunomeloThursday, September 22, 2011
  • 51. 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/crabThursday, September 22, 2011
  • 52. 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 ChartsThursday, September 22, 2011
  • 53. 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 FuriousThursday, September 22, 2011
  • 54. How are we working ? Sprints, Online Discussions and Issues https://github.com/muricoca/crab/wiki/UpcomingEventsThursday, September 22, 2011
  • 55. 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 ....Thursday, September 22, 2011
  • 56. 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 :(Thursday, September 22, 2011
  • 57. RecDay: Recomendações diariamente!Thursday, September 22, 2011
  • 58. Thursday, September 22, 2011
  • 59. Thursday, September 22, 2011
  • 60. DicasThursday, September 22, 2011
  • 61. Items Recomendados Toby Segaran, Programming Collective SatnamAlag, Collective Intelligence in Intelligence, OReilly, 2007 Action, Manning Publications, 2009 Sites como TechCrunch e ReadWriteWebThursday, September 22, 2011
  • 62. 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 LearningThursday, September 22, 2011
  • 63. Onde você estará em tudo isso ? Fonte: Hunch.com Obrigado !!Thursday, September 22, 2011
  • 64. Sistemas de Recomendação Marcel Pinheiro Caraciolo mpc@cin.ufpe.br / marcel@orygens.com/marcel@recday.com @marcelcaracioloThursday, September 22, 2011