A DNN-Based Cross-Domain Recommender System for Alleviating Cold-Start Problem in E-Commerce

IEEE Open Journal of the Industrial Electronics Society ◽

10.1109/ojies.2020.3012627 ◽

2020 ◽

Vol 1 ◽

pp. 194-206

Author(s):

Hanxin Wang ◽

Daichi Amagata ◽

Takuya Makeawa ◽

Takahiro Hara ◽

Niu Hao ◽

...

Keyword(s):

Recommender System ◽

Cross Domain ◽

Cold Start Problem

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Preliminary Investigation of Alleviating User Cold-Start Problem in E-commerce with Deep Cross-Domain Recommender System

Companion Proceedings of The 2019 World Wide Web Conference on - WWW '19 ◽

10.1145/3308560.3316596 ◽

2019 ◽

Author(s):

Hanxin Wang ◽

Daichi Amagata ◽

Takuya Maekawa ◽

Takahiro Hara ◽

Hao Niu ◽

...

Keyword(s):

Recommender System ◽

Preliminary Investigation ◽

Cross Domain ◽

Cold Start Problem

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Solving Cold-Start Problem by Combining Personality Traits and Demographic Attributes in a User Based Recommender System

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i4/01420 ◽

2017 ◽

Vol 7 (5) ◽

pp. 231-239 ◽

Author(s):

◽

Zewengel Tilahun ◽

Huang Dong Jun ◽

Ammar Oad ◽

◽

...

Keyword(s):

Personality Traits ◽

Recommender System ◽

Demographic Attributes ◽

Cold Start Problem

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Inter-departmental research collaboration recommender system based on content filtering in a cold start problem

2017 IEEE 10th International Workshop on Computational Intelligence and Applications (IWCIA) ◽

10.1109/iwcia.2017.8203581 ◽

2017 ◽

Author(s):

Diana Purwitasari ◽

Chastine Fatichah ◽

I Ketut Eddy Purnama ◽

Surya Sumpeno ◽

Mauridhi Hery Purnomo

Keyword(s):

Recommender System ◽

Research Collaboration ◽

Content Filtering ◽

Cold Start Problem

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Handling User Cold-Start Problem for Group Recommender System Using Social Behaviour Wise Group Detection Method

2018 International Conference on Research in Intelligent and Computing in Engineering (RICE) ◽

10.1109/rice.2018.8509054 ◽

2018 ◽

Author(s):

Pooja R. Ghodsad ◽

P. N. Chatur

Keyword(s):

Recommender System ◽

Social Behaviour ◽

Detection Method ◽

Group Detection ◽

Group Recommender ◽

Cold Start Problem

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An Ontology Based Recommender System to Mitigate the Cold Start Problem in Personalized Web Search

Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1 - Smart Innovation, Systems and Technologies ◽

10.1007/978-3-319-63673-3_15 ◽

2017 ◽

pp. 120-127

Author(s):

Kamlesh Makwana ◽

Jay Patel ◽

Parth Shah

Keyword(s):

Recommender System ◽

Cold Start Problem

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A two-stage cross-domain recommendation for cold start problem in cyber-physical systems

2015 International Conference on Machine Learning and Cybernetics (ICMLC) ◽

10.1109/icmlc.2015.7340669 ◽

2015 ◽

Author(s):

Panpan Liu ◽

Jingjing Cao ◽

Xiaolei Liang ◽

Wenfeng Li

Keyword(s):

Cyber Physical Systems ◽

Two Stage ◽

Physical Systems ◽

Cross Domain ◽

Cold Start Problem

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A recommender system to address the Cold Start problem for App usage prediction

International Journal of Machine Learning and Cybernetics ◽

10.1007/s13042-018-0864-z ◽

2018 ◽

Vol 10 (9) ◽

pp. 2257-2268

Author(s):

Di Han ◽

Jianqing Li ◽

Lei Yang ◽

Zihua Zeng

Keyword(s):

Recommender System ◽

Cold Start Problem

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Comparing product specifications to solve the cold start problem in a recommender system

2016 XLII Latin American Computing Conference (CLEI) ◽

10.1109/clei.2016.7833354 ◽

2016 ◽

Author(s):

Silvana Aciar ◽

Gabriela Aciar ◽

Debbie Zhang

Keyword(s):

Recommender System ◽

Product Specifications ◽

Cold Start Problem

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Exploiting User Demographic Attributes for Solving Cold-Start Problem in Recommender System

Lecture Notes on Software Engineering ◽

10.7763/lnse.2013.v1.66 ◽

2013 ◽

pp. 303-307 ◽

Author(s):

Laila Safoury ◽

Akram Salah

Keyword(s):

Recommender System ◽

Demographic Attributes ◽

Cold Start Problem

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An Empirical Study on Effect of Semantic Measures in Cross-Domain Recommender System in User Cold-Start Scenario

Knowledge Science, Engineering and Management - Lecture Notes in Computer Science ◽

10.1007/978-3-030-82147-0_22 ◽

2021 ◽

pp. 264-278

Author(s):

Yuhan Wang ◽

Qing Xie ◽

Lin Li ◽

Yongjian Liu

Keyword(s):

Empirical Study ◽

Recommender System ◽

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