Improving Serendipity and Accuracy in Cross-Domain Recommender Systems

Lecture Notes in Business Information Processing - Web Information Systems and Technologies ◽

10.1007/978-3-319-66468-2_6 ◽

2017 ◽

pp. 105-119 ◽

Cited By ~ 1

Author(s):

Denis Kotkov ◽

Shuaiqiang Wang ◽

Jari Veijalainen

Keyword(s):

Recommender Systems ◽

Cross Domain

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Tags and Item Features as a Bridge for Cross-Domain Recommender Systems

Procedia Computer Science ◽

10.1016/j.procs.2017.12.080 ◽

2018 ◽

Vol 125 ◽

pp. 624-631 ◽

Cited By ~ 9

Author(s):

Ashish K. Sahu ◽

Pragya Dwivedi ◽

Vibhor Kant

Keyword(s):

Recommender Systems ◽

Cross Domain

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Context-Aware Techniques for Cross-Domain Recommender Systems

2015 Brazilian Conference on Intelligent Systems (BRACIS) ◽

10.1109/bracis.2015.42 ◽

2015 ◽

Author(s):

Douglas Veras ◽

Ricardo Prudencio ◽

Carlos Ferraz ◽

Alysson Bispo ◽

Thiago Prota

Keyword(s):

Recommender Systems ◽

Context Aware ◽

Cross Domain

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Demographic Inference Via Knowledge Transfer in Cross-Domain Recommender Systems

2018 IEEE International Conference on Data Mining (ICDM) ◽

10.1109/icdm.2018.00162 ◽

2018 ◽

Cited By ~ 2

Author(s):

Jin Shang ◽

Mingxuan Sun ◽

Kevyn Collins-Thompson

Keyword(s):

Knowledge Transfer ◽

Recommender Systems ◽

Cross Domain ◽

Demographic Inference

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Cross-Domain Recommender Systems

2011 IEEE 11th International Conference on Data Mining Workshops ◽

10.1109/icdmw.2011.57 ◽

2011 ◽

Cited By ~ 57

Author(s):

Paolo Cremonesi ◽

Antonio Tripodi ◽

Roberto Turrin

Keyword(s):

Recommender Systems ◽

Cross Domain

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User Models Development Based on Cross-Domain for Recommender Systems

Proceedings of the 22nd Brazilian Symposium on Multimedia and the Web - Webmedia '16 ◽

10.1145/2976796.2988211 ◽

2016 ◽

Cited By ~ 1

Author(s):

Marivaldo Bispo Rodrigues ◽

Gabriela O. Mota da Silva ◽

Frederico Araújo Durão

Keyword(s):

Recommender Systems ◽

User Models ◽

Cross Domain

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Knowledge transfer by domain-independent user latent factor for cross-domain recommender systems

Future Generation Computer Systems ◽

10.1016/j.future.2020.02.024 ◽

2020 ◽

Vol 108 ◽

pp. 320-333

Author(s):

Ashish Kumar Sahu ◽

Pragya Dwivedi

Keyword(s):

Knowledge Transfer ◽

Recommender Systems ◽

Latent Factor ◽

Cross Domain ◽

Domain Independent

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RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems

IEEE Transactions on Neural Networks and Learning Systems ◽

10.1109/tnnls.2019.2907430 ◽

2020 ◽

Vol 31 (8) ◽

pp. 2731-2740 ◽

Cited By ~ 8

Author(s):

Cheng Wang ◽

Mathias Niepert ◽

Hui Li

Keyword(s):

Recommender Systems ◽

Adversarial Networks ◽

Cross Domain

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Aligned Intrinsic User Factors Knowledge Transfer for Cross-domain Recommender Systems

Procedia Computer Science ◽

10.1016/j.procs.2020.03.237 ◽

2020 ◽

Vol 167 ◽

pp. 363-372

Author(s):

Ashish Kumar Sahu ◽

Pragya Dwivedi

Keyword(s):

Knowledge Transfer ◽

Recommender Systems ◽

Cross Domain

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CD-CARS: Cross-domain context-aware recommender systems

Expert Systems with Applications ◽

10.1016/j.eswa.2019.06.020 ◽

2019 ◽

Vol 135 ◽

pp. 388-409 ◽

Cited By ~ 5

Author(s):

Douglas Véras ◽

Ricardo Prudêncio ◽

Carlos Ferraz

Keyword(s):

Recommender Systems ◽

Context Aware ◽

Cross Domain

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Advanced Recommender Systems

Advances in Computer and Electrical Engineering - Advanced Methodologies and Technologies in Network Architecture, Mobile Computing, and Data Analytics ◽

10.4018/978-1-5225-7598-6.ch022 ◽

2019 ◽

pp. 299-311 ◽

Cited By ~ 1

Author(s):

Young Park

Keyword(s):

Recommender Systems ◽

Future Research ◽

Context Aware ◽

Research Topics ◽

Systematic Overview ◽

Cross Domain ◽

Group Recommender Systems ◽

Group Recommender

This chapter presents a brief and systematic overview of four major advanced recommender systems: group recommender systems, context-aware recommender systems, multi-criteria recommender systems, and cross-domain recommender systems. These advanced recommendations are characterized and compared in a unifying model as extensions of basic recommender systems. Future research topics and directions in the area of advanced personalized recommendations are discussed. Advanced recommender technologies will continue to advance.

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