Cross-Domain RS: Survey

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fabiopaiva
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Description

Survey sobre o estado da arte de Sistemas de Recomendadores Cross-Domain.

Resource summary

Cross-Domain RS: Survey
1 Abordagens baseadas em Modelo de Usuário
1.1 Cross-system user modeling and personalization on the social web (Abel et al, 2011)
1.2 Mediation of user models for enhanced personalization in recommender systems (Berkovsky et al, 2008)
1.3 A multi-agent smart user model for cross-domain recommender systems (Gonzáles et al, 2005)
1.4 Semantic modelling of user interests based on cross-folksonomy analysis (Szomszor et al, 2008)
1.5 Cross domain recommendation based on multi-type media fusion (Tan et al, 2013)
1.6 Contextualization, User Modeling and Personalization in the Social Web (Doutorado: Abel, 2011)
1.7 Exploram modelos de usuário para capturar as preferências sobre itens dos domínios envolvidos.
2 Abordagens para estabelecer relações entre características do domínio
2.1 Cross-domain recommender systems (Cremonesi et al, 2011)
2.2 Tags as Bridges between Domains: Improving Recommendation with Tag-Induced Cross-Domain Collaborative Filtering (Shi et al, 2011)
2.3 CrosSing: A framework to develop knowledge-based recommenders in cross domains(Mestrado: Azak, 2010)
2.4 How to recommend music to film buffs: enabling the provision of recommendations from multiple domains (Doutorado: Loizou, 2009)
2.5 A generic semantic-based framework for cross-domain recommendation (Tobias et al, 2011)
2.6 Location-adapted music recommendation using tags (Kaminskas and Ricci, 2011)
2.7 Normalmente aplicada a relações de domínio baseada em conteúdo
2.8 A semantic-based framework for building cross-domain networks: Application to item recommendation (Mestrado: Tobías et al, 2013)
2.9 Building Ontologies for Cross-domain Recommendation on Facial Skin Problem and Related Cosmetics (Moe & Aung, 2014)
2.9.1 Um método para criar ontologias aplicas em cross-domain.
2.10 Context Aware Cross-domain based Recommendation (Moe & Aung, 2014)
2.10.1 O contexto é construído a partir das respostas que o usuário dá ao sistema. Por exemplo: a) há manchas brancas no seu rosto? b) O centro delas é escuro?
2.11 A Framework for Cross-domain Recommendation in Folksonomies (Guo & Chen, 2013)
2.12 Cold-Start Management with Cross-Domain Collaborative Filtering and Tags (Enrich et al,2013)
2.13 Exploraram relações explícitas entre características dos domínios.
3 Abordagens baseadas em Transfer Learning
3.1 Improving Users’ Acceptance in Recommender System (Doutorado: Wei, 2013)
3.1.1 Framework que integra informações de redes sociais com dados de cross-domain.
3.2 Can movies and books collaborate? Cross-domain collaborative filtering for sparsity reduction (Li et al, 2009)
3.3 Recentemente tem sido aplicadas à Filtragem Colaborativa.
3.4 Transfer learning for collaborative filtering via a rating-matrix generative model (Li et al, 2009)
3.5 Transfer learning in collaborative filtering for sparsity reduction (Pan et al, 2010)
3.6 Multi-domain collaborative filtering (Zhang et al, 2010)
3.7 Cross-domain Recommendations based on semantically-enhanced User Web Behavior (Doutorado: Hoxha, 2014)
3.7.1 Propõe um modelo formal do comportamento de navegação do usuário
3.7.2 Desenvolvimento de um mecanismo para garantir a diversidade de recomendações de vários domínios.
3.7.3 Um modelo probabilístico multi-relacional usado para facilitar a transferência de conhecimento entre domínios de sistemas CF
3.7.4 O autor observou que não há trabalhos que envolvem as seguintes características de um RS: a) CB + CF, b) Incorporação de representação semântica no conteúdo e; c) Recomendações cross-domain
3.8 Transfer Learning for Content-Based Recommender Systems using Tree Matching (Biadsy et al, 2013)
3.9 Active Transfer Learning for Cross-System Recommendation (Zhao et al, 2013)
3.10 TALMUD - Transfer Learning for Multiple Domains (Moreno et al, 2012)
3.11 Twin Bridge Transfer Learning for Sparse Collaborative Filtering (Shi et al, 2013)
4 Tutorial on Cross-domain Recommender Systems (Cantador & Cremonesi, 2014)
4.1 Níveis de domínio
4.1.1 Atributo
4.1.2 Tipo
4.1.3 Item
4.1.4 Sistema
4.2 Tarefas de recomendação
4.2.1 Multi-domain
4.2.2 Linked-domain
4.2.3 Cross-domain
4.3 Técnicas
4.3.1 Linking/aggregating knowledge
4.3.1.1 Merging user preferences
4.3.1.2 Mediating user modeling data
4.3.1.3 Combining recommendations
4.3.1.4 Linking domains
4.3.2 Sharing/transferring knowledge
4.3.2.1 Sharing latents features
4.3.2.2 Transferring rating patterns
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