Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/89892
Título: | Improving group recommendations using personality, dynamic clustering and Multi-Agent microServices |
Autor(es): | Alves, Patrícia Martins, André Novais, Paulo Marreiros, Goreti |
Palavras-chave: | Affective computing Dynamic clustering Group recommender systems Leisure tourism Multi-Agent microservices Personality |
Data: | 14-Set-2023 |
Resumo(s): | The complexity associated to group recommendations needs strategies to mitigate several problems, such as the group's heterogeinity and conflicting preferences, the emotional contagion phenomenon, the cold-start problem, and the group members' needs and concerns while providing recommendations that satisfy all members at once. In this demonstration, we show how we implemented a Multi-Agent Microservice to model the tourists in a mobile Group Recommender System for Tourism prototype and a novel dynamic clustering process to help minimize the group's heterogeneity and conflicting preferences. To help solve the cold-start problem, the preliminary tourist attractions preference and travel-related preferences & concerns are predicted using the tourists' personality, considering the tourists' disabilities and fears/phobias. Although there is no need for data from previous interactions to build the tourists' profile since we predict the tourists' preferences, the tourist agents learn with each other by using association rules to find patterns in the tourists' profile and in the ratings given to Points of Interest to refine the recommendations. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/89892 |
ISBN: | 9798400702419 |
DOI: | 10.1145/3604915.3610653 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
---|---|---|---|---|
recsys23-preprint.pdf | 464,99 kB | Adobe PDF | Ver/Abrir |