Alike people, alike interests? A large-scale study on interest similarity in social networks

Xiao, Han; Wang, Leye; Park, Soochang; Rumín, Rubén Cuevas; Crespi, Noël

doi:10.1109/asonam.2014.6921631

Cited by 19 publications

(11 citation statements)

References 13 publications

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“…Hence, for the attribute with a value of vector, like favorite music, movies and TV shows where users present multiple items, we apply the cosine similarity to describe two users' interest similarity. For the detailed description about how to calculate cosine similarity between two users' interests, readers can refer to our previous work [13]. According to the Figure 1(e), we verify that users with similar interests link to each other with high probability, which is also observed by other work [10][11] [14].…”

Section: Derived Featuressupporting

confidence: 80%

“…To the authors' best knowledge, this is the first work to address the new-IEEE ICC 2015 SAC -Social Networking 978-1-4673-6432-4/15/$31.00 ©2015 IEEE user link prediction problem by leveraging the information from a single-platform. (2) To evaluate our approach, we use a Facebook data set including 479,000 users [13]. For each user, we record his demographics, interests and links (friends).…”

Section: Introductionmentioning

confidence: 99%

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Link prediction for new users in Social Networks

Xiao¹,

Wang²,

Han³

et al. 2015

2015 IEEE International Conference on Communications (ICC)

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International audienceLink prediction for new users who have not created any link is a fundamental problem in Online Social Networks (OSNs). It can be used to recommend friends for new users to start building their social networks. The existing studies use crossplatform approaches to predict a new user's links on a certain OSN by porting his existing links from other OSNs. However, it cannot work when OSNs are not willing to share their data or users do not want to connect different OSN accounts. In this paper, we use a single-platform approach to carry out the link prediction. We explore the users' profile attributes (e.g., workplace, high school and hometown) which can be easily obtained during the new users' sign up procedure. Based on the limited available information from the new user, along with the attributes and links from existing users, we extract three types of social features: basic feature, derived feature and latent relation feature. We propose a link prediction model using these social features based on Support Vector Machines. Eventually, we rely on a large Facebook data set consisting of 479; 000 users to evaluate our proposed model. The result reveals that our model outperforms the baselines by achieving the AUC value of 0:83; it also demonstrates that each of the proposed social features contribute significantly to the prediction mode

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Section: Derived Featuressupporting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Link prediction for new users in Social Networks

Xiao¹,

Wang²,

Han³

et al. 2015

2015 IEEE International Conference on Communications (ICC)

Self Cite

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“…It has been reported that user interests' similarity can be leveraged to support services and products recommendation [22]. A user prefers to choose the items recommended by other users with similar interest in a social network.…”

Section: Interest Similarity Computationmentioning

confidence: 99%

“…A user prefers to choose the items recommended by other users with similar interest in a social network. Exploring those users of a high interest similarity with the existing clients could efficiently enlarge client groups for cloud service providers [22].…”

Section: Interest Similarity Computationmentioning

confidence: 99%

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Context‐Aware Cloud Service Selection Model for Mobile Cloud Computing Environments

2018

Wireless Communications and Mobile Computing

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Mobile cloud computing (MCC) has attracted extensive attention in recent years. With the prevalence of MCC, how to select trustworthy and high quality mobile cloud services becomes one of the most urgent problems. Therefore, this paper focuses on the trustworthy service selection and recommendation in mobile cloud computing environments. We propose a novel service selection and recommendation model (SSRM), where user similarity is calculated based on user context information and interest. In addition, the relational degree among services is calculated based on PropFlow algorithm and we utilize it to improve the accuracy of ranking results. SSRM supports a personalized and trusted selection of cloud services through taking into account mobile user's trust expectation. Simulation experiments are conducted on ns3 simulator to study the prediction performance of SSRM compared with other two traditional approaches. The experimental results show the effectiveness of SSRM.

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Cooperative caching strategy based mobile vehicle social‐aware in internet of vehicles

Kan

Xing

2023

Trans Emerging Tel Tech

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The dramatic growth of smart in‐vehicle applications in the Internet of Vehicles and the increasing quality of experience for vehicle users have put a huge traffic load on the mobile core network. Mobile edge caching has been proposed to place content at the edge of the network to serve users in close proximity to them. However, the dynamic topology of existing edge networks and the inherent storage limitations of edge devices pose a serious challenge to vehicular edge caching. Therefore, in this article, we propose a cooperative caching strategy based on mobile prediction and social awareness that allows collaborative content decision making among edge devices. Specifically, the long short‐term memory network is used to predict vehicle trajectories, social relationships are computed using content similarity and contact rates among vehicle users to select vehicles that can serve as caching nodes, and deep reinforcement learning is employed to achieve the final caching decision. Simulation results show that the caching strategy proposed in this article achieves up to 7.7%, 24.2%, and 27.3% gains, respectively, in terms of content acquirement delay compared to the reinforcement learning algorithm, the algorithm without considering mobility, and the non‐cooperative algorithm.

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Alike people, alike interests? A large-scale study on interest similarity in social networks

Cited by 19 publications

References 13 publications

Link prediction for new users in Social Networks

Link prediction for new users in Social Networks

Context‐Aware Cloud Service Selection Model for Mobile Cloud Computing Environments

Cooperative caching strategy based mobile vehicle social‐aware in internet of vehicles

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