How friends affect user behaviors? An exploration of social relation analysis for recommendation

Yuan, Ting; Cheng, Jian; Zhang, Xi; Liu, Qingshan; Lu, Hanqing

doi:10.1016/j.knosys.2015.08.005

Cited by 44 publications

(19 citation statements)

References 47 publications

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“…A few works have been done based on heterogeneous friends' influence in social recommendations [10,[29][30][31]. Friendships on social networks are different from trust relationships because friendships are bidirectional, and the interests of friends are heterogeneous [32].…”

Section: Related Workmentioning

confidence: 99%

“…In addition to user-item ratings and user-user relationships, some additional information from online user behaviours can also be combined with recommender systems. In [31], the authors considered different influences of friends and different levels of willingness to be influenced in social recommendations. The influence of friends and different levels of willingness to be influenced are generated by using a social influence propagation method on social networks.…”

Section: Related Workmentioning

confidence: 99%

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Topological Influence‐Aware Recommendation on Social Networks

Xiong

Wang

et al. 2019

Complexity

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Users in online networks exert different influence during the process of information propagation, and the heterogeneous influence may contribute to personalized recommendations. In this paper, we analyse the topology of social networks to investigate users’ influence strength on their neighbours. We also exploit the user-item rating matrix to find the importance of users’ ratings and determine their influence on entire social networks. Based on the local influence between users and global influence over the whole network, we propose a recommendation method with indirect interactions that makes adequate use of users’ relationships on social networks and users’ rating data. The two kinds of influence are incorporated into a matrix factorization framework. We also consider indirect interactions between users who do not have direct links with each other. Experimental results on two real-world datasets demonstrate that our proposed framework performs better than other state-of-the-art methods for all users and cold-start users. Compared with node degrees, betweenness, and clustering coefficients, coreness constitutes the best topological descriptor to identify users’ local influence, and recommendations with the measure of coreness outperform other descriptors of user influence.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Topological Influence‐Aware Recommendation on Social Networks

Xiong

Wang

et al. 2019

Complexity

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“…On the other hand, many research works in both sociology and computer science [28]- [31] claimed a general principle in friendship or positive link prediction: people tend to make friends with other similar people. The issue is how should we measure the similarity between people.…”

Section: A Local Influencementioning

confidence: 99%

Link-Sign Prediction in Dynamic Signed Directed Networks

Dang

Ignat

2018

2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC)

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Many real-world applications can be modeled as signed directed graphs wherein the links between nodes can have either positive or negative signs. Social networks can be modeled as signed directed graphs where positive/negative links represent trust/distrust relationships between users. In order to predict user behavior in social networks, several studies have addressed the link-sign prediction problem that predicts a link sign as positive or negative. However, the existing approaches do not take into account the time when the links were added which plays an important role in understanding the user relationships. Moreover, most of the existing approaches require the complete network information which is not realistic in modern social networks. Last but not least, these approaches are not adapted for dynamic networks and the link-sign prediction algorithms have to be reapplied each time the network changes. In this paper, we study the problem of link-sign prediction by combining random walks for graph sampling, Doc2Vec for node vectorization and Recurrent Neural Networks for prediction. The approach requires only local information and can be trained incrementally. Our experiments on the same datasets as state-ofthe-art approaches show an improved prediction. Index Terms-link-sign prediction, dynamic networks, recurrent neural networks, random walks, Doc2Vec • Epinions 1 is a product-rating social network [9]. Users of Epinions can write reviews and rate products. They also

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“… apply the influence of each user and the centrality theory to calculate the rank of the social media content. Inspired by the idea in , we introduce the influence of tweets to evaluate the active degree of a user, called tweet influence. Tweet _ influ ( u j ) denotes the tweet influence of user u j , which is calculated as follows:

italicTweet_italicinflu (u_{j}) = \frac{italicTweet_italicnum (u_{j}) - italicMIN}{italicMAX - italicMIN}, u_{j} \in italicFriend (u_{i})

…”

Section: The Trust Friends Computing Modelmentioning

confidence: 99%

“…The problem of top-k recommendation is proposed, and there are a lot of studies on it. Yuan et al [14] introduce a unified framework that applies the influence of social relationship to 4 of 20 L. CUI ET AL. recommendation by guidance of buddies (those friends who have strong influence on a user) and susceptibility (the willingness to be influenced ) mining.…”

Section: Related Workmentioning

confidence: 99%

A video recommendation algorithm based on the combination of video content and social network

Cui

Dong

et al. 2016

Concurrency and Computation

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Summary Recently, social network has been one of the biggest information exchange platforms of the Internet. Moreover, the users in social network used to watch videos through social network application. To provide a proper recommended video list, the video recommendation algorithm for social network is becoming a hot research issue. On one hand, more and more researchers introduce the concept of trust into video recommendation algorithms. However, most of them only select the trust friends based on the similarity and neglect the characteristics of social network. On the other hand, most previous video recommendation algorithms are only based on the number that a video is viewed to evaluate a video's quality. They do not make good use of the social relationship in social network and the video's reputation. This paper mainly focuses on the challenge that the effectiveness and performance of current video recommendation algorithm in social network cannot satisfy the users. In this paper, we propose a novel video recommendation algorithm based on the combination of video content and social network. Our proposed algorithm consists of the trust friends computing model and video's quality evaluation model. The trust friends computing method takes into account similarity between users, interaction between users, and the active degree of a user. In our video's quality evaluation model, we combine the acceptance ratio of a video with a video's reputation. The video can be given an appropriate rating score through this model. We design corresponding trust friends computing algorithm and video recommendation algorithm respectively for two proposed models. Our integral video recommendation algorithm consists of these two algorithms. The experimental results indicate that the performance and effectiveness of our algorithm are better than those of two classical video recommendation algorithms (i.e., user‐based collaborative filtering algorithm and TBR‐d algorithm), in terms of precision, recall and F1‐measure. Copyright © 2016 John Wiley & Sons, Ltd.

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How friends affect user behaviors? An exploration of social relation analysis for recommendation

Cited by 44 publications

References 47 publications

Topological Influence‐Aware Recommendation on Social Networks

Topological Influence‐Aware Recommendation on Social Networks

Link-Sign Prediction in Dynamic Signed Directed Networks

A video recommendation algorithm based on the combination of video content and social network

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