A survey on personality-aware recommendation systems

Dhelim, Sahraoui; Aung, Nyothiri; Bouras, Mohammed Amine; Ning, Huansheng; Wang, Zhaoxia

doi:10.1007/s10462-021-10063-7

Cited by 97 publications

(37 citation statements)

References 184 publications

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“…As well observed by M. Jackson and R. , "personality is one of the best known, and potentially the most useful, psychological concepts in tourism", and therefore can be useful in different areas of RS (Tkalcic & Chen, 2015) to help overcome challenges related to user modeling (Dhelim et al, 2021), since it is strongly related to the users' preferences (Cantador, Fernández-Tobías, & Bellogín, 2013; Martijn, Conati, & Verbert, 2022), i.e., users with similar personalities tend to choose similar items or contents (Cantador et al, 2013). For example, extraverts who are dependent on warmth and gregariousness tend to enjoy popular music, and persons who score high on excitement-seeking tend to prefer rock music (Cantador et al, 2013;Rawlings & Ciancarelli, 1997).…”

Section: Introductionmentioning

confidence: 84%

“…The correlation between the tourists' personality and preferred tourist attractions can be valuable to automatically model their pro le in (G)RS for tourism, solving the cold-start problem and eliminating the need for users to ll long and tedious questionnaires and con gurations (Dhelim et al, 2021;Gretzel, Mitsche, Hwang, & Fesenmaier, 2006;Tkalcic et al, 2011). But could personality help nd and automatically model other characteristics of tourists, such as their motivations, and travel-related preferences and concerns when travelling in leisure?…”

Section: Neuroticism Extraversion Openness To Experiencementioning

confidence: 99%

“…Interestingly, these advances are coincident with the sudden increase of interest for personality computing in the early 2000s (Vinciarelli & Mohammadi, 2014), and consequently to the recent proliferation of personalityaware RS (Dhelim, Aung, Bouras, Ning, & Cambria, 2021), which can easily be explained by its own de nition: "personality is the sum-total of the actual or potential behavior-patterns of the organism, as determined by heredity and environment" (Eysenck, 1998), meaning each individual has her own behavior patterns, which are considered relatively stable over time across different situations (McCrae & Costa Jr, 1997), i.e., "an individual's behavior naturally varies somewhat from occasion to occasion, but… there is a core of consistency which de nes the individual's 'true nature'" (Matthews, Deary, & Whiteman, 2003). These behavior patterns were summarized into ve universal personality dimensions by Costa and MacCrae (1992): Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Neuroticism, being the Five Factor Model (FFM), or Big Five, recognized as the most widely accepted model to represent them (Dhelim et al, 2021;Digman, 1990;Matz, Chan, & Kosinski, 2016), and therefore were adopted in this study. Each factor is de ned by six traits/facets (Costa & MacCrae, 1992), resulting in a total of 30 traits, which are more granular and can be used to better characterize a person (see Table 1).…”

Section: Introductionmentioning

confidence: 99%

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Group Recommender Systems for Tourism: How does Personality predicts Preferences for Attractions, Travel Motivations, Preferences and Concerns?

Alves

Martins

Saraiva

et al. 2022

Preprint

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To travel in leisure is an emotional experience, and therefore, the more information about the tourist is known, the more personalized recommendations of places and attractions can be made. But if to provide recommendations to a tourist is complex, to provide them to a group is even more. The emergence of personality computing and personality-aware recommender systems (RS) brought a new solution for the cold-start problem inherent to the conventional RS, and can be the leverage needed to solve conflicting preferences in heterogenous groups and make more precise and personalized recommendations to tourists, as it has been evidenced that personality is strongly related to preferences in many domains, including tourism. Although many studies on psychology of tourism can be found, not many predict the tourists’ preferences based on the Big Five personality dimensions. This work aims to find how personality relates to the choice of a wide range of tourist attractions, travelling motivations, and travel-related preferences and concerns, hoping to provide a solid base for researchers in the tourism RS area to automatically model tourists in the system without the need for tedious configurations, and solve the cold-start problem and conflicting preferences. By performing Exploratory and Confirmatory Factor Analysis on the data gathered from an online questionnaire, sent to Portuguese individuals from different areas of formation and age groups (n = 1035), we found all five personality dimensions can help predict the choice of tourist attractions and travel-related preferences and concerns, and that only Neuroticism and Openness predict travelling motivations.

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Section: Introductionmentioning

confidence: 84%

Section: Neuroticism Extraversion Openness To Experiencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Group Recommender Systems for Tourism: How does Personality predicts Preferences for Attractions, Travel Motivations, Preferences and Concerns?

Alves

Martins

Saraiva

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In addition to the conventional social networks, the Metaverse is integrated with users' social context provided by Social Internet of Things (SIoT) services [24]. SIoT services are customized according to the user's social features; for example personality-aware recommendation systems [25] and user interest mining based services [26], [27] can offer social enhanced SIoT services.…”

Section: A Architecturementioning

confidence: 99%

Edge-enabled Metaverse: The Convergence of Metaverse and Mobile Edge Computing

dhelim¹,

Kechadi²,

Chen³

et al. 2022

Preprint

Self Cite

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The Metaverse is a virtual environment where users are represented by avatars to navigate a virtual world, which has strong links with the physical one. State-of-the-art Metaverse architectures rely on a cloud-based approach for avatar physics emulation and graphics rendering computation. Such centralized design is unfavorable as it suffers from several drawbacks caused by the long latency required for cloud access, such as low quality visualization. To solve this issue, in this paper, we propose a Fog-Edge hybrid computing architecture for Metaverse applications that leverage an edge-enabled distributed computing paradigm, which makes use of edge devices computing power to fulfil the required computational cost for heavy tasks such as collision detection in virtual universe and computation of 3D physics in virtual simulation. The computational cost related to an entity in the Metaverse such as collision detection or physics emulation are performed at the end-device of the associated physical entity. To prove the effectiveness of the proposed architecture, we simulate a distributed social metaverse application. Simulation results shows that the proposed architecture can reduce the latency by 50\% when compared with the legacy cloud-based Metaverse applications.

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“…Aung et al [20], [21], [22] studied trust relationship among driverless cars in the context of vehicular ad-hoc networks (VANET) for route recommendations and path planning. Ning et al [23], [24] studied the trust among social network users for personality-aware recommendation system, they concluded that recommendation accuracy can be significantly improved by adding social factors such as trust and personality traits.…”

Section: Related Workmentioning

confidence: 99%

Trust2Vec: Large-Scale IoT Trust Management System based on Signed Network Embeddings

Dhelim,

Kechadi,

Aung

et al. 2022

Preprint

Self Cite

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A trust management system (TMS) is an integral component of any IoT network. A reliable trust management system must guarantee the network security, data integrity, and act as a referee that promotes legitimate devices, and punishes any malicious activities. Trust scores assigned by TMSs reflect devices' reputations, which can help predict the future behaviours of network entities and subsequently judge the reliability of different network entities in IoT networks. Many TMSs have been proposed in the literature, these systems are designed for smallscale trust attacks, and can deal with attacks where a malicious device tries to undermine TMS by spreading fake trust reports. However, these systems are prone to large-scale trust attacks. To address this problem, in this paper, we propose a TMS for large-scale IoT systems called Trust2Vec, which can manage trust relationships in large-scale IoT systems and can mitigate largescale trust attacks that are performed by hundreds of malicious devices. Trust2Vec leverages a random-walk network exploration algorithm that navigates the trust relationship among devices and computes trust network embeddings, which enables it to analyze the latent network structure of trust relationships, even if there is no direct trust rating between two malicious devices. To detect large-scale attacks, suck as self-promoting and bad-mouthing, we propose a network embeddings community detection algorithm that detects and blocks communities of malicious nodes. The effectiveness of Trust2Vec is validated through large-scale IoT network simulation. The results show that Trust2Vec can achieve up to 94% mitigation rate in various network scenarios.

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A survey on personality-aware recommendation systems

Cited by 97 publications

References 184 publications

Group Recommender Systems for Tourism: How does Personality predicts Preferences for Attractions, Travel Motivations, Preferences and Concerns?

Group Recommender Systems for Tourism: How does Personality predicts Preferences for Attractions, Travel Motivations, Preferences and Concerns?

Edge-enabled Metaverse: The Convergence of Metaverse and Mobile Edge Computing

Trust2Vec: Large-Scale IoT Trust Management System based on Signed Network Embeddings

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