Friend Recommendation Algorithm Based on User Activity and Social Trust in LBSNs

Chengcheng, Su; Yu, Yao; Sui, Mingfei; Zhang, Haijun

doi:10.1109/wisa.2015.11

Cited by 2 publications

(2 citation statements)

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“…Su et al discussed a scenario how the friend's suggestions are displayed to the user's screen on social networking sites. The authors have taken an example of friend social networking site and discussed the concept of calculating the matching index based on similar user preferences [2]. Zhou has given an idea for the recommended system for blogs in early days of blog Web 2.0 tool.…”

Section: Related Workmentioning

confidence: 99%

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Adaptive and Personalized Web Blog Searching Technique Using S-ANFIS

et al. 2022

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Day by day, the number of blog users and microblog users is increasing worldwide. It is easy to say that blogs have captured a significant portion of other web services. In the past few years, the number of users has exponentially increased. User count of Facebook, Twitter, and Instagram applications is not hidden from anyone. Users on such platforms share ideas, experiences, stories, opinions, and views and want to interact with people with the same set of interests. As per the user’s expectation, there is a requirement of two things: content curation and recommendations. The content curation algorithm will find the people and their posts on personalized search results. In addition, the recommendation system will help to find the most appropriate match to interact with. In this paper, both approaches are combined to show the user’s curated and recommended results. The article focuses on the hybrid model named S-ANFIS, and the results are compared with the well-known approaches like ANN, Deep Neural Network (DNN), and Recurrent Neural Network (RNN).

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Section: Related Workmentioning

confidence: 99%

“…Instagram, Twitter, and Facebook are wellknown examples of multimedia social networking blogs. Su et al discussed how the users of such applications want to interact with other users from their locality and similar user preferences [2]. Every day, millions of users are using the services of these platforms and share the posts.…”

Section: Introductionmentioning

confidence: 99%