Implicit Social Networks for Social Recommendation of Scholarly Papers

2017 ◽  
pp. 79-92 ◽  
Author(s):  
Shaikhah Alotaibi ◽  
Julita Vassileva
Keyword(s):  
Social Networks ◽  
Social Recommendation

User opinions driven social recommendation system

2021 ◽  
Vol 25 (1) ◽  
pp. 21-31
Author(s):  
Lakshmikanth Paleti ◽  
P. Radha Krishna ◽  
J.V.R. Murthy
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Social Recommendation ◽  
Novel Approach ◽  
Tripartite Graphs ◽  
Content Based Filtering

Recommendation systems provide reliable and relevant recommendations to users and also enable users’ trust on the website. This is achieved by the opinions derived from reviews, feedbacks and preferences provided by the users when the product is purchased or viewed through social networks. This integrates interactions of social networks with recommendation systems which results in the behavior of users and user’s friends. The techniques used so far for recommendation systems are traditional, based on collaborative filtering and content based filtering. This paper provides a novel approach called User-Opinion-Rating (UOR) for building recommendation systems by taking user generated opinions over social networks as a dimension. Two tripartite graphs namely User-Item-Rating and User-Item-Opinion are constructed based on users’ opinion on items along with their ratings. Proposed approach quantifies the opinions of users and results obtained reveal the feasibility.

scholarly journals Information dissemination model based on multi-dimensional node influence in social networks

2020 ◽  
Author(s):  
Haiming Wu ◽  
Ruigang Wang ◽  
Lixia Jia ◽  
Likui Feng ◽  
Xu Zhou
Keyword(s):  
Social Networks ◽  
Epidemic Model ◽  
Information Dissemination ◽  
State Transition ◽  
User Interaction ◽  
Multiple Linear Regression Model ◽  
Three Dimensions ◽  
Social Recommendation ◽  
Factors Affecting ◽  
Model Based

Abstract Social network has gradually become the mainstream way for people to obtain and interact with information. The study on the law of information dissemination in social networks is of great significance to enterprise marketing, public opinion control and social recommendation. This paper puts forward a method that use multi-dimensional node influence and epidemic model to illustrate the causes and rules of information dissemination in social networks. Firstly, based on the multiple linear regression model, a measurement method of node influence is proposed from three dimensions: topology, user interaction behavior and information content. Then, taking the node influence as the cause of state transition, the information dissemination model based on the epidemic model is constructed, and the multidimensional factors affecting the information dissemination are analyzed. Meanwhile, the information dissemination trend in social networks is described.

scholarly journals Deep Adversarial Social Recommendation

2019 ◽  
Author(s):  
Wenqi Fan ◽  
Tyler Derr ◽  
Yao Ma ◽  
Jianping Wang ◽  
Jiliang Tang ◽  
...  
Keyword(s):  
Social Networks ◽  
Recommender Systems ◽  
Daily Life ◽  
Sampling Technique ◽  
Representation Learning ◽  
Mapping Method ◽  
Social Recommendation ◽  
Adversarial Learning ◽  
Social Domain ◽  
Real World Datasets

Recent years have witnessed rapid developments on social recommendation techniques for improving the performance of recommender systems due to the growing influence of social networks to our daily life. The majority of existing social recommendation methods unify user representation for the user-item interactions (item domain) and user-user connections (social domain). However, it may restrain user representation learning in each respective domain, since users behave and interact differently in the two domains, which makes their representations to be heterogeneous. In addition, most of traditional recommender systems can not efficiently optimize these objectives, since they utilize negative sampling technique which is unable to provide enough informative guidance towards the training during the optimization process. In this paper, to address the aforementioned challenges, we propose a novel deep adversarial social recommendation framework DASO. It adopts a bidirectional mapping method to transfer users' information between social domain and item domain using adversarial learning. Comprehensive experiments on two real-world datasets show the effectiveness of the proposed framework.

scholarly journals Matrix Factorization Recommendation Algorithm Based on Multiple Social Relationships

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Sheng Bin ◽  
Gengxin Sun
Keyword(s):  
Social Networks ◽  
Recommender Systems ◽  
Social Relationships ◽  
Matrix Factorization ◽  
Social Relationship ◽  
Recommendation System ◽  
Social Recommendation ◽  
Recommendation Algorithm ◽  
Recommendation Algorithms

With the widespread use of social networks, social recommendation algorithms that add social relationships between users to recommender systems have been widely applied. Existing social recommendation algorithms only introduced one type of social relationship to the recommendation system, but in reality, there are often multiple social relationships among users. In this paper, a new matrix factorization recommendation algorithm combined with multiple social relationships is proposed. Through experiment results analysis on the Epinions dataset, the proposed matrix factorization recommendation algorithm has a significant improvement over the traditional and matrix factorization recommendation algorithms that integrate a single social relationship.

scholarly journals Social recommendation model based on user interaction in complex social networks

PLoS ONE ◽  
2019 ◽  
Vol 14 (7) ◽  
pp. e0218957 ◽  
Author(s):  
Yakun Li ◽  
Jiaomin Liu ◽  
Jiadong Ren
Keyword(s):  
Social Networks ◽  
User Interaction ◽  
Social Recommendation ◽  
Model Based

Social Recommendation for Social Networks Using Deep Learning Approach: A Systematic Review

2021 ◽  
pp. 15-29
Author(s):  
Muhammad Alrashidi ◽  
Ali Selamat ◽  
Roliana Ibrahim ◽  
Ondrej Krejcar
Keyword(s):  
Systematic Review ◽  
Social Networks ◽  
Deep Learning ◽  
Learning Approach ◽  
Social Recommendation

scholarly journals Social Recommendation System Based on Hypergraph Attention Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhongxiu Xia ◽  
Weiyu Zhang ◽  
Ziqiang Weng
Keyword(s):  
Neural Networks ◽  
Social Networks ◽  
Recommendation System ◽  
Recommendation Systems ◽  
High Order ◽  
Social Recommendation ◽  
Attention Network ◽  
Practical Applications ◽  
Order Relations ◽  
Graph Neural Networks

In recent years, due to the rise of online social platforms, social networks have more and more influence on our daily life, and social recommendation system has become one of the important research directions of recommendation system research. Because the graph structure in social networks and graph neural networks has strong representation capabilities, the application of graph neural networks in social recommendation systems has become more and more extensive, and it has also shown good results. Although graph neural networks have been successfully applied in social recommendation systems, their performance may still be limited in practical applications. The main reason is that they can only take advantage of pairs of user relations but cannot capture the higher-order relations between users. We propose a model that applies the hypergraph attention network to the social recommendation system (HASRE) to solve this problem. Specifically, we take the hypergraph’s ability to model high-order relations to capture high-order relations between users. However, because the influence of the users’ friends is different, we use the graph attention mechanism to capture the users’ attention to different friends and adaptively model selection information for the user. In order to verify the performance of the recommendation system, this paper carries out analysis experiments on three data sets related to the recommendation system. The experimental results show that HASRE outperforms the state-of-the-art method and can effectively improve the accuracy of recommendation.

scholarly journals Hierarchical Social Recommendation Model Based on a Graph Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhongqin Bi ◽  
Lina Jing ◽  
Meijing Shan ◽  
Shuming Dou ◽  
Shiyang Wang
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Social Relationship ◽  
Recommendation Systems ◽  
Absolute Error ◽  
User Preferences ◽  
Social Recommendation ◽  
Public Datasets ◽  
The Relationship ◽  
Different Levels

With the continuous accumulation of social network data, social recommendation has become a widely used recommendation method. Based on the theory of social relationship propagation, mining user relationships in social networks can alleviate the problems of data sparsity and the cold start of recommendation systems. Therefore, integrating social information into recommendation systems is of profound importance. We present an efficient network model for social recommendation. The model is based on the graph neural network. It unifies the attention mechanism and bidirectional LSTM into the same framework and uses a multilayer perceptron. In addition, an embedded propagation method is added to learn the neighbor influences of different depths and extract useful neighbor information for social relationship modeling. We use this method to solve the problem that the current research methods of social recommendation only extract the superficial level of social networks but ignore the importance of the relationship strength of the users at different levels in the recommendation. This model integrates social relationships into user and project interactions, not only capturing the weight of the relationship between different users but also considering the influence of neighbors at different levels on user preferences. Experiments on two public datasets demonstrate that the proposed model is superior to other benchmark methods with respect to mean absolute error and root mean square error and can effectively improve the quality of recommendations.

Sign in / Sign up

Export Citation Format

Share Document