Transfer learning in heterogeneous collaborative filtering domains

By Weike Pan, Qiang Yang. 2013

174 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yan Wang
2. Zhong Ming
3. Jie Lu
4. Yaochu Jin
5. Qian Zhang
6. Congfu Xu
7. Hao Zhong
8. Chaochao Chen

In Information Systems

1. Yan Wang
2. Zhong Ming
3. Jie Lu
4. Yaochu Jin
5. Qian Zhang
6. Congfu Xu
7. Hao Zhong
8. Chaochao Chen

Citations in the corpus

• A Comprehensive Survey on Transfer Learning (2019, neutral): “There are some other studies about cross-domain recommendation [194]–[197].”
• Cross-Domain Recommender Systems (2015, background): “• Rating latent feature sharing, which aims to factorize single-domain rating matrices using latent features, share latent feature spaces across domains, and transfer knowledge between domains through the latent feature matrices [50, 51, 52, 53].”
• Compressed knowledge transfer via factorization machine for heterogeneous collaborative recommendation (2015, methodology): “Illustration of compressed knowledge transfer (CKT) via factorization and auxiliary data, which can thus be categorized as a frontal-side transfer learning setting [23], rather than the two-side [22], user-side [7] or item-side [29] knowledge transfer setting.”
• Cross-Domain Recommendation: Challenges, Progress, and Prospects (2021, methodology): “After this, the MDR models proposed in [ Cao et al. , 2010; Moreno et al. , 2012; Pan and Yang, 2013; Zhang et al. , 2016 ] employ different techniques, i.e., feature combination, transfer learning, and active learning to transfer the knowledge of similar/common users among multiple domains.”
• Artificial intelligence in recommender systems (2020, methodology): “Transfer collective factorization (TCF) [119] has been developed to use implicit data in the source domain to help the prediction of explicit feedback, i.e., ratings in the target domain.”
• A Survey on Session-based Recommender Systems (2019, unknown): “Although transfer learning has been well explored in conventional RSs like collaborative filtering [60, 75], it is rarely explored in SBRSs.”
• Adaptive Bayesian personalized ranking for heterogeneous implicit feedbacks (2015, background): “Heterogeneous feedbacks usually refer to a situation with more than one type of users’ feedbacks such as 5-star numerical ratings and like/dislike binary scores as that in transfer by collective factorization (TCF) [22], in which transfer learning [18] techniques have played an important role.”