Are heterogeinity and conflicting preferences no longer a problem? Personality-based dynamic clustering for group recommender systems

Alves, Patrícia; Martins, André; Negrão, Francisco; Novais, Paulo; Almeida, Ana; Marreiros, Goreti

doi:10.1016/j.eswa.2024.124812

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2024

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Cited by 3 publications

References 64 publications

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Exploring a social-curiosity-based algorithm for group recommender systems

Tseng,

Liang,

Huang

2024

J Supercomput

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Exploring a social-curiosity-based algorithm for group recommender systems

Tseng,

Liang,

Huang

2024

J Supercomput

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Research on Personalized Recommendation of Complementary Products Based on Demand Cross-Elasticity and Hypergraphs

Duan,

Du,

Shang

2024

Electronics

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To improve recommendation systems, it is essential to enhance both their practicality and accuracy, thereby supporting users in making informed shopping decisions. Incorporating two types of product relationships can effectively achieve these goals: first, the product relationships, like complements, and second, the social relationships among users. However, existing studies have paid little attention to user-side information or item-side information. This paper proposes a product recommendation model that utilizes cross-elasticity of demand and hypergraphs, referred to as Hg-CR. First, users and items build a hypergraph. The user–item interactions form the hyperedges. Also, users build a hypergraph between themselves based on their social relationships. Second, hypergraph attention networks (HANs) learn the relationships between nodes. They capture the key features of nodes and hyperedges with a high degree of adaptability. A community detection algorithm organizes users into groups for product recommendations by assessing their similarities. Within different communities, individuals seek complementary products based on the cross-elasticity theory of demand. Additionally, we provide recommendations for complementary products. Tests on real datasets show that the Hg-CR model is about 10% more accurate than the other baseline models.

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Determinants of Tourist Preferences

Dubey,

Guleria

2024

Advances in E-Business Research

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This research explores the evolving dynamics of tourist preferences between online travel agents (OTAs) and traditional travel agents (TTAs) in the era of digital consumerism. By analysing current affairs and real-world case studies, the study examines vital determinants such as convenience, trust, price sensitivity, and personalization that influence consumer choices. It highlights how technological innovations, including AI, mobile applications, and user-generated content, are shaping modern tourist behaviour while traditional agents adapt by focusing on personalised, high-end services. The findings provide insights into the future trajectory of the tourism industry, offering a comparative analysis of both models and their respective roles in the evolving digital ecosystem.

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Are heterogeinity and conflicting preferences no longer a problem? Personality-based dynamic clustering for group recommender systems

Cited by 3 publications

References 64 publications

Exploring a social-curiosity-based algorithm for group recommender systems

Exploring a social-curiosity-based algorithm for group recommender systems

Research on Personalized Recommendation of Complementary Products Based on Demand Cross-Elasticity and Hypergraphs

Determinants of Tourist Preferences

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