Personality and Social Trust in Group Recommendations

Quijano-Sánchez, Lara; Recio-Garcí­a, Juan A.; Dí­az-Agudo, Belén

doi:10.1109/ictai.2010.92

Cited by 38 publications

(47 citation statements)

References 10 publications

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“…Though some papers [4][5][6][7]9] considered the social factors such as personality, trust, personal expertise, and social status of the members; once the information was acquired, the steps of the social networking group recommendation systems were as follows: At first, fetching the list of preference of all group members by individual recommendation system; then, increasing or decreasing the weight of a group of members of the article via social factors; finally, getting the ultimate results of group recommendation systems by some aggregation strategies, which consider the individual recommendation and weight. These methods utilize the social factors more or less; group members' weight is considered in ones; it means that a few of members would be ignored because of some group members play a decisive role, leading to the tendency to influence influential members.…”

Section: Social Network Recommendationmentioning

confidence: 99%

“…As to previous group recommendations in social networks, they consider the strength of the relationship between group members [4,5] and the impact of social network information on each group member [7][8][9], hence generating group recommendation by the aggregation strategy. At present, the main impact of the social network on the group recommendation systems is the social influence of the group members.…”

Section: Grs Calculation Frameworkmentioning

confidence: 99%

“…The current social network group recommendation systems consider both the strength of the relationship between the members of the group into account [4][5][6] and the influence of social network information on each group members [7][8][9] and finally generate group recommenders through aggregation strategies. At present, the main influence of social network is the social impact of the group members on the group recommendation systems.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, whole group members are asked to be in the same social network on present social network 2 Wireless Communications and Mobile Computing recommendation system [4,5,[7][8][9], and the members of other social networks could be ignored. The method proposed in this paper is that some users who dislike communicating with others on social network and are not in the same social network can benefit from this as well.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Group Recommendation Systems Based on External Social‐Trust Networks

Fang

Jiang

et al. 2018

Wireless Communications and Mobile Computing

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With the development of social networks and online mobile communities, group recommendation systems support users' interaction with similar interests or purposes with others. We often provide some advices to the close friends, such as listening to favorite music and sharing favorite dishes. However, users' personalities have been ignored by the traditional group recommendation systems while the majority is satisfied. In this paper, a method of group recommendation based on external socialtrust networks is proposed, which builds a group profile by analyzing not only users' preferences, but also the social relationships between members inside and outside of the group. We employ the users' degree of disagreement to adjust group preference rating by external information of social-trust network. Moreover, having a discussion about different social network utilization ratio, we proposed a method to work for smaller group size. The experimental results show that the proposed method has consistently higher precision and leads to satisfactory recommendations for groups.

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Section: Social Network Recommendationmentioning

confidence: 99%

Section: Grs Calculation Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Group Recommendation Systems Based on External Social‐Trust Networks

Fang

Jiang

et al. 2018

Wireless Communications and Mobile Computing

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show abstract

“…Besides, circumstantial life events, influences, and surroundings can further change our social abilities [9]. In our research, we study how to model these social-cognitive processes in human decision-making processes [10,11]. Specifically, we have designed a method that is aware of the different personalities and social ties that group members present.…”

Section: Introductionmentioning

confidence: 99%

Development of a group recommender application in a Social Network

Quijano-Sánchez

Díaz-Agudo

Recio-García

2014

Knowledge-Based Systems

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In today's society, recommendations are becoming increasingly important. With the advent of the Social Web and the growing popularity of Social Networks, where users explicitly provide personal information and interact with others and the system, it is becoming clear that the key for the success of recommendations is to develop new strategies which focus on social recommendations leveraged by these new sources of knowledge. In our work, we focus on group recommender systems. These systems traditionally suffer from a number of shortcomings that hamper their effectiveness. In this paper we continue our research, that focuses on improving the overall quality of group recommendations through the addition of social knowledge to existing recommendation strategies. To do so, we use the information stored in Social Networks to elicit social factors following two approaches: the cognitive modeling approach, that studies how people's way of thinking predisposes their actions; and the social approach, that studies how people's relationships predispose their actions. We show the value of using models of social cognition extracted from Social Networks in group recommender systems through the instantiation of our model into a real-life Facebook movie recommender application.

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Social identity in trusting artificial intelligence agents: Evidence from lab and online experiments

Sun,

Xu,

2024

Manage Decis Econ

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This paper explores human trust in artificial intelligence (AI), focusing on the effects of social categorization (ingroup vs. outgroup) and AI human‐likeness through two pre‐registered studies involving 160 participants each. The first study, a lab experiment in China, and the second, an online experiment representative of the United States, both utilized a trust game to assess trust across four conditions: ingroup‐humanoid AI, ingroup‐non‐humanoid AI, outgroup‐humanoid AI, and outgroup‐non‐humanoid AI. Results indicated higher trust for ingroup and humanoid AIs, with statistical significance. Mixed‐design ANOVA was used to analyze the data, revealing significant main effects and interactions. The second study also identified an emotional connection as a mediator in trust, suggesting significant design implications for AI in trust‐critical sectors like healthcare and autonomous transportation.

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Personality and Social Trust in Group Recommendations

Cited by 38 publications

References 10 publications

Group Recommendation Systems Based on External Social‐Trust Networks

Group Recommendation Systems Based on External Social‐Trust Networks

Development of a group recommender application in a Social Network

Social identity in trusting artificial intelligence agents: Evidence from lab and online experiments

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