Cross-Domain RS: Survey

Abordagens baseadas em Modelo de Usuário
1. Cross-system user modeling and personalization on the social web (Abel et al, 2011)
2. Mediation of user models for enhanced personalization in recommender systems (Berkovsky et al, 2008)
3. A multi-agent smart user model for cross-domain recommender systems (Gonzáles et al, 2005)
4. Semantic modelling of user interests based on cross-folksonomy analysis (Szomszor et al, 2008)
5. Cross domain recommendation based on multi-type media fusion (Tan et al, 2013)
6. Contextualization, User Modeling and Personalization in the Social Web (Doutorado: Abel, 2011)
7. Exploram modelos de usuário para capturar as preferências sobre itens dos domínios envolvidos.
Abordagens para estabelecer relações entre características do domínio
1. Cross-domain recommender systems (Cremonesi et al, 2011)
2. Tags as Bridges between Domains: Improving Recommendation with Tag-Induced Cross-Domain Collaborative Filtering (Shi et al, 2011)
3. CrosSing: A framework to develop knowledge-based recommenders in cross domains(Mestrado: Azak, 2010)
4. How to recommend music to film buffs: enabling the provision of recommendations from multiple domains (Doutorado: Loizou, 2009)
5. A generic semantic-based framework for cross-domain recommendation (Tobias et al, 2011)
6. Location-adapted music recommendation using tags (Kaminskas and Ricci, 2011)
7. Normalmente aplicada a relações de domínio baseada em conteúdo
8. A semantic-based framework for building cross-domain networks: Application to item recommendation (Mestrado: Tobías et al, 2013)
9. Building Ontologies for Cross-domain Recommendation on Facial Skin Problem and Related Cosmetics (Moe & Aung, 2014)
  1. Um método para criar ontologias aplicas em cross-domain.
10. Context Aware Cross-domain based Recommendation (Moe & Aung, 2014)
  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?
11. A Framework for Cross-domain Recommendation in Folksonomies (Guo & Chen, 2013)
12. Cold-Start Management with Cross-Domain Collaborative Filtering and Tags (Enrich et al,2013)
13. Exploraram relações explícitas entre características dos domínios.
Abordagens baseadas em Transfer Learning
1. Improving Users’ Acceptance in Recommender System (Doutorado: Wei, 2013)
  1. Framework que integra informações de redes sociais com dados de cross-domain.
2. Can movies and books collaborate? Cross-domain collaborative filtering for sparsity reduction (Li et al, 2009)
3. Recentemente tem sido aplicadas à Filtragem Colaborativa.
4. Transfer learning for collaborative filtering via a rating-matrix generative model (Li et al, 2009)
5. Transfer learning in collaborative filtering for sparsity reduction (Pan et al, 2010)
6. Multi-domain collaborative filtering (Zhang et al, 2010)
7. Cross-domain Recommendations based on semantically-enhanced User Web Behavior (Doutorado: Hoxha, 2014)
  1. Propõe um modelo formal do comportamento de navegação do usuário
  2. Desenvolvimento de um mecanismo para garantir a diversidade de recomendações de vários domínios.
  3. Um modelo probabilístico multi-relacional usado para facilitar a transferência de conhecimento entre domínios de sistemas CF
  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
8. Transfer Learning for Content-Based Recommender Systems using Tree Matching (Biadsy et al, 2013)
9. Active Transfer Learning for Cross-System Recommendation (Zhao et al, 2013)
10. TALMUD - Transfer Learning for Multiple Domains (Moreno et al, 2012)
11. Twin Bridge Transfer Learning for Sparse Collaborative Filtering (Shi et al, 2013)
Tutorial on Cross-domain Recommender Systems (Cantador & Cremonesi, 2014)
1. Níveis de domínio
  1. Atributo
  2. Tipo
  3. Item
  4. Sistema
2. Tarefas de recomendação
  1. Multi-domain
  2. Linked-domain
  3. Cross-domain
3. Técnicas
  1. Linking/aggregating knowledge
    1. Merging user preferences
    2. Mediating user modeling data
    3. Combining recommendations
    4. Linking domains
  2. Sharing/transferring knowledge
    1. Sharing latents features
    2. Transferring rating patterns

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Cross-Domain RS: Survey

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