Personality-based and trust-aware products recommendation in social networks

Vatani, Nasim; Rahman, Amir Masoud; Javadi, Hamid Haj Seyyed

doi:10.1007/s10489-022-03542-z

Cited by 11 publications

(4 citation statements)

References 40 publications

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“…Amazon website: This dataset is related to the metadata of products and the review information provided by users about various products. 31 Epinions: This dataset contains the product review information where users provide the ratings for items and present their own review. Moreover, social network of trust is established based on specifying the trusted people by each user.…”

Section: Evaluation Datasetsmentioning

confidence: 99%

“…Amazon website: This dataset is related to the metadata of products and the review information provided by users about various products 31 …”

Section: Main Concepts and Associated Terminologiesmentioning

confidence: 99%

“…A personality‐based recommendation method utilizing the trust factor was proposed by Vatani et al 31 First, more trusty similar users to the target user were determined based on their personality‐based purchasing records. The similarity value of these reliable similar users also varied according to different categories.…”

Section: Primary Studies' Categorization Schemesmentioning

confidence: 99%

See 2 more Smart Citations

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%

“…Amazon website: This dataset is related to the metadata of products and the review information provided by users about various products 31 …”

Section: Main Concepts and Associated Terminologiesmentioning

confidence: 99%

Section: Primary Studies' Categorization Schemesmentioning

confidence: 99%

See 1 more Smart Citation

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

Vatani,

Rahmani,

Javadi

et al. 2023

Int J Communication

View full text Add to dashboard Cite

show abstract

“…An illustration of a social networking website is Facebook [1]. The objectives of this suggestion are marginally different from those of a product recommendation [22]. While growing the number of connections enhances the experience and promotes network growth, increasing the number of product recommendations on sites such as Facebook, Twitter, and Instagram can boost a merchant's profit by enabling product sales; as a result, the network can grow and connect more effectively with the help of recommendations from possible friends (or links) in social network analysis known as Link Recommendation [23].…”

Section: Link Recommendation Systemmentioning

confidence: 99%