Group oriented trust-aware location recommendation for location-based social networks

Teoman, Huseyin Alper; Karagöz, Pınar

doi:10.1145/3477314.3507154

Cited by 3 publications

(4 citation statements)

References 21 publications

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“…Friendships are achieved utilizing tagged information in these visits so that users tagged in the same visit are considered friends with each other. 13 Gowalla: This dataset is a location-based social network site where the provided information by users called checking-ins enables sharing of their visited locations. 45 Geolife: This dataset is related to the users' trajectory over a specified period of time, containing not only their daily routines but also their hobbies and sports activities.…”

Section: Evaluation Datasetsmentioning

confidence: 99%

“…Prediction speed: It measures the execution time or achieved response time of a recommendation system for providing suggestions 13 …”

Section: Main Concepts and Associated Terminologiesmentioning

confidence: 99%

“…In more detail, each check‐in visit contains location id, user id, location category, date and time, longitude and latitude and tagged users in these visits. Friendships are achieved utilizing tagged information in these visits so that users tagged in the same visit are considered friends with each other 13 …”

Section: Main Concepts and Associated Terminologiesmentioning

confidence: 99%

“…Teoman et al 13 provided a location‐based recommendation approach using a trust factor that recommends interesting locations to a group of users. To this end, they used random walk with restart algorithm according to the present location of experts, group members and trustworthy users.…”

Section: Primary Studies' Categorization Schemesmentioning

confidence: 99%

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Social networks data analytical approaches for trust‐based recommender systems: A systematic literature review

Vatani,

Rahmani,

Javadi

et al. 2023

Int J Communication

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SummaryWith the explosive growth of information on the web and the quick provision of novel web services, recommendation systems have emerged as efficient mechanism for providing advice regarding items in which users might be potentially interested. However, a traditional recommender system, which solely mines the user's previous behavior and item descriptions for recommendations, has some drawbacks. To overcome these limitations, a new solution that has recently garnered significant attention is using trust data. This approach, however, presents challenges in utilizing suitable trust data according to recommender systems applications, underlying social network structures, and user needs. In addition, in a selective decision‐making system, trust, as a kind of social network data, plays a significant role and needs an appropriate approach for making inferences. This article provides a systematic literature review of the current trust‐based social recommender systems. It also presents a detailed categorization of the trust type utilized and inferred from the existing trust‐related social networks data analytical approaches. Furthermore, it addresses the main properties and challenges of the most popular trust‐based social recommendation systems. Finally, it presents our findings and discusses open issues that provide researchers with insight to develop more enhanced recommender systems.

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Section: Evaluation Datasetsmentioning

confidence: 99%

“…Prediction speed: It measures the execution time or achieved response time of a recommendation system for providing suggestions 13 …”

Section: Main Concepts and Associated Terminologiesmentioning

confidence: 99%

Section: Main Concepts and Associated Terminologiesmentioning

confidence: 99%

Section: Primary Studies' Categorization Schemesmentioning

confidence: 99%

See 2 more Smart Citations