A survey on personality-aware recommendation systems
| Autor: | Nyothiri Aung, Huansheng Ning, Mohammed Amine Bouras, Erik Cambria, Sahraoui Dhelim |
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| Rok vydání: | 2021 |
| Předmět: |
Social and Information Networks (cs.SI)
FOS: Computer and information sciences Linguistics and Language Point (typography) Computer Science - Artificial Intelligence Computer science media_common.quotation_subject Personality computing Computer Science - Social and Information Networks Recommender system Personality psychology Data science Language and Linguistics Field (computer science) Personality modeling Computer Science - Information Retrieval Computer Science - Computers and Society Artificial Intelligence (cs.AI) Cold start Artificial Intelligence Computers and Society (cs.CY) Personality Information Retrieval (cs.IR) media_common |
| Zdroj: | Artificial Intelligence Review. 55:2409-2454 |
| ISSN: | 1573-7462 0269-2821 |
| DOI: | 10.1007/s10462-021-10063-7 |
| Popis: | With the emergence of personality computing as a new research field related to artificial intelligence and personality psychology, we have witnessed an unprecedented proliferation of personality-aware recommendation systems. Unlike conventional recommendation systems, these new systems solve traditional problems such as the cold start and data sparsity problems. This survey aims to study and systematically classify personality-aware recommendation systems. To the best of our knowledge, this survey is the first that focuses on personality-aware recommendation systems. We explore the different design choices of personality-aware recommendation systems, by comparing their personality modeling methods, as well as their recommendation techniques. Furthermore, we present the commonly used datasets and point out some of the challenges of personality-aware recommendation systems. Comment: The final version is published in Artificial Intelligence Review (2021) https://link.springer.com/article/10.1007/s10462-021-10063-7 |
| Databáze: | OpenAIRE |
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