A Survey of Link Recommendation for Social Networks: Methods, Theoretical Foundations, and Future Research Directions

Li, Zhepeng; Fang, Xiao; Sheng, Olivia R. Liu

doi:10.2139/ssrn.2682561

Cited by 28 publications

(34 citation statements)

References 82 publications

(153 reference statements)

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“…To have a base line, we started from the most commonly used measures of similarity [13] which were introduced in the previous section, cosine (1) similarity, common neighbors (2), Jaccard (3) similarity and Adamic-Adar (4).…”

Section: Evaluation Of Existing Methodsmentioning

confidence: 99%

Evaluations of Similarity Measures on VK for Link Prediction

Lee

Tukhvatov

2018

Data Sci. Eng.

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Recommender system is one of the most important components for many companies and social networks such as Facebook and YouTube. A recommendation system consists of algorithms which allow to predict and recommend friends or products. This paper studies to facilitate finding like-minded people with same interests in social networks. In our research, we used real data from the most popular social network in Russia, VK (Vkontakte). The study is motivated on the assumption that similarity breeds connection. We evaluate well-known similarity measures in the field on our collected VK datasets and find limited performance results. The result shows that majority of users in VK tend not to add possible users with whom they have common acquaintances. We also propose a topology-based similarity measure to predict future friends. Then, we compare our results with the results of other well-known methods and discuss differences.

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Section: Evaluation Of Existing Methodsmentioning

confidence: 99%

Evaluations of Similarity Measures on VK for Link Prediction

Lee

Tukhvatov

2018

Data Sci. Eng.

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“…Behind this power, we find a great importance related to the development of link recommendation features and handling the social graph basing on the topology of existing links and leveraging quantities such as node degree and edge density [13].…”

Section: Social Network Recommendationmentioning

confidence: 99%

Deep Learning-Based Recommendation: Current Issues and Challenges

Fakhfakh¹,

Ammar²,

Amar³

2017

ijacsa

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Abstract-Due to the revolutionary advances of deep learning achieved in the field of computer vision, object recognition and natural language processing, the deep learning gained much attention. The recommendation task is influenced by the deep learning trend which shows its significant effectiveness. The deep learning based recommender models provides a better detention of user preferences, item features and users-items interactions history. In this paper, we provide a recent literature review of researches dealing with deep learning based recommendation approaches which is preceded by a presentation of the main lines of the recommendation approaches and the deep learning techniques. Then we finish by presenting the recommendation approach adopted by the most popular video recommendation platform YouTube which is based essentially on deep learning advances.

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“…At the high level, methodologies dealing with friend recommendation can be grouped into three categories [22]: classi cation, ing and ranking. e classi cation methods are based on extracted features, e.g., features between two nodes like path-based metric Katz [14] or neighbor-based metric Adamic/Adar [23].…”

Section: Related Workmentioning

confidence: 99%

“…From the algorithmic perspective, existing methodologies for the friend recommendation problem roughly fall into three categories: classi cation, ing, and ranking [10,22]. e classi cation method treats friendship between users as a binary classi cation problem, and trains a classi er to predict the likelihood of a friendship to be created between users based on pre-de ned features.…”

Section: Introductionmentioning

confidence: 99%

BayDNN

Ding

et al. 2017

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

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Friend recommendation is a critical task in social networks. In this paper, we propose a Bayesian Personalized Ranking Deep Neural Network (BayDNN) model for friend recommendation in social networks. BayDNN rst extracts latent structural pa erns from the input network data and then use the Bayesian ranking to make friend recommendations. With BayDNN we achieve signi cant performance improvement on two public datasets: Epinions and Slashdot. For example, on Epinions dataset, BayDNN signi cantly outperforms the state-of-the-art algorithms, with a 5% improvement on NDCG over the best baseline. e advantages of the proposed BayDNN mainly come from a novel Bayesian personalized ranking (BPR) idea, which precisely captures the users' personal bias based on the extracted deep features, and its underlying convolutional neural network (CNN), which o ers a mechanism to extract latent deep structural feature representations of the complicated network data. To get good parameter estimation for the neural network, we present a netuned pre-training strategy for the proposed BayDNN model based on Poisson and Bernoulli probabilistic models.

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A Survey of Link Recommendation for Social Networks: Methods, Theoretical Foundations, and Future Research Directions

Cited by 28 publications

References 82 publications

Evaluations of Similarity Measures on VK for Link Prediction

Evaluations of Similarity Measures on VK for Link Prediction

Deep Learning-Based Recommendation: Current Issues and Challenges

BayDNN

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