scholarly journals A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Rongmei Zhao ◽  
Xi Xiong ◽  
Xia Zu ◽  
Shenggen Ju ◽  
Zhongzhi Li ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Recommendation System ◽  
Information Overload ◽  
User Behavior ◽  
Recommendation Systems ◽  
Social Domain ◽  
Network Information ◽  
Cross Domain ◽  
Real World Datasets

Search engines and recommendation systems are an essential means of solving information overload, and recommendation algorithms are the core of recommendation systems. Recently, the recommendation algorithm of graph neural network based on social network has greatly improved the quality of the recommendation system. However, these methods paid far too little attention to the heterogeneity of social networks. Indeed, ignoring the heterogeneity of connections between users and interactions between users and items may seriously affect user representation. In this paper, we propose a hierarchical attention recommendation system (HA-RS) based on mask social network, combining social network information and user behavior information, which improves not only the accuracy of recommendation but also the flexibility of the network. First, learning the node representation in the item domain through the proposed Context-NE model and then the feature information of neighbor nodes in social domain is aggregated through the hierarchical attention network. It can fuse the information in the heterogeneous network (social domain and item domain) through the above two steps. We propose the mask mechanism to solve the cold-start issues for users and items by randomly masking some nodes in the item domain and in the social domain during the training process. Comprehensive experiments on four real-world datasets show the effectiveness of the proposed method.

scholarly journals BANANA: when Behavior ANAlysis meets social Network Alignment

2020 ◽  
Author(s):  
Fuxin Ren ◽  
Zhongbao Zhang ◽  
Jiawei Zhang ◽  
Sen Su ◽  
Li Sun ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Behavior Analysis ◽  
User Behavior ◽  
Network Alignment ◽  
Learning Performance ◽  
User Behavior Analysis ◽  
The Social ◽  
End To End ◽  
Real World Datasets

Recently, aligning users among different social networks has received significant attention. However, most of the existing studies do not consider users’ behavior information during the aligning procedure and thus still suffer from the poor learning performance. In fact, we observe that social network alignment and behavior analysis can benefit from each other. Motivated by such an observation, we propose to jointly study the social network alignment problem and user behavior analysis problem. We design a novel end-to-end framework named BANANA. In this framework, to leverage behavior analysis for social network alignment at the distribution level, we design an earth mover’s distance based alignment model to fuse users’ behavior information for more comprehensive user representations. To further leverage social network alignment for behavior analysis, in turn, we design a temporal graph neural network model to fuse behavior information in different social networks based on the alignment result. Two models above can work together in an end-to-end manner. Through extensive experiments on real-world datasets, we demonstrate that our proposed approach outperforms the state-of-the-art methods in the social network alignment task and the user behavior analysis task, respectively.

scholarly journals An Information Recommendation Technique Based on Influence and Activeness of Users in Social Networks

2021 ◽  
Vol 11 (6) ◽  
pp. 2530
Author(s):  
Minsoo Lee ◽  
Soyeon Oh
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Recommendation Systems ◽  
Network Services ◽  
The Past ◽  
User Behaviors ◽  
User Influence ◽  
User Activity ◽  
Information Recommendation ◽  
Social Network Services

Over the past few years, the number of users of social network services has been exponentially increasing and it is now a natural source of data that can be used by recommendation systems to provide important services to humans by analyzing applicable data and providing personalized information to users. In this paper, we propose an information recommendation technique that enables smart recommendations based on two specific types of analysis on user behaviors, such as the user influence and user activity. The components to measure the user influence and user activity are identified. The accuracy of the information recommendation is verified using Yelp data and shows significantly promising results that could create smarter information recommendation systems.

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 PRUB: A Privacy Protection Friend Recommendation System Based on User Behavior

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Jiang ◽  
Ruijin Wang ◽  
Zhiyuan Xu ◽  
Yaodong Huang ◽  
Shuo Chang ◽  
...  
Keyword(s):  
Social Network ◽  
Recommendation System ◽  
User Behavior ◽  
Network Services ◽  
Mobile Social Network ◽  
Fine Grained ◽  
Friend Recommendation ◽  
Social Applications ◽  
Browsing Behavior ◽  
Behavioral Information

The fast developing social network is a double-edged sword. It remains a serious problem to provide users with excellent mobile social network services as well as protecting privacy data. Most popular social applications utilize behavior of users to build connection with people having similar behavior, thus improving user experience. However, many users do not want to share their certain behavioral information to the recommendation system. In this paper, we aim to design a secure friend recommendation system based on the user behavior, called PRUB. The system proposed aims at achieving fine-grained recommendation to friends who share some same characteristics without exposing the actual user behavior. We utilized the anonymous data from a Chinese ISP, which records the user browsing behavior, for 3 months to test our system. The experiment result shows that our system can achieve a remarkable recommendation goal and, at the same time, protect the privacy of the user behavior information.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

Predictive Analytics of Social Networks

2018 ◽  
pp. 823-862
Author(s):  
Ming Yang ◽  
William H. Hsu ◽  
Surya Teja Kallumadi
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Predictive Analytics ◽  
Concept Drift ◽  
Customer Relationship ◽  
Community Formation ◽  
Heterogeneous Information ◽  
Network Information ◽  
Partial Data ◽  
Business To Consumer

In this chapter, the authors survey the general problem of analyzing a social network in order to make predictions about its behavior, content, or the systems and phenomena that generated it. They begin by defining five basic tasks that can be performed using social networks: (1) link prediction; (2) pathway and community formation; (3) recommendation and decision support; (4) risk analysis; and (5) planning, especially causal interventional planning. Next, they discuss frameworks for using predictive analytics, availability of annotation, text associated with (or produced within) a social network, information propagation history (e.g., upvotes and shares), trust, and reputation data. They also review challenges such as imbalanced and partial data, concept drift especially as it manifests within social media, and the need for active learning, online learning, and transfer learning. They then discuss general methodologies for predictive analytics involving network topology and dynamics, heterogeneous information network analysis, stochastic simulation, and topic modeling using the abovementioned text corpora. They continue by describing applications such as predicting “who will follow whom?” in a social network, making entity-to-entity recommendations (person-to-person, business-to-business [B2B], consumer-to-business [C2B], or business-to-consumer [B2C]), and analyzing big data (especially transactional data) for Customer Relationship Management (CRM) applications. Finally, the authors examine a few specific recommender systems and systems for interaction discovery, as part of brief case studies.

Knowledge Discovery from Online Communities

Data Mining ◽  
2013 ◽  
pp. 1230-1252
Author(s):  
Luca Cagliero ◽  
Alessandro Fiori
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
User Behavior ◽  
Network Models ◽  
Social Model ◽  
User Generated Content ◽  
Growth Trend ◽  
User Behaviors ◽  
Application Systems

This chapter presents an overview of social network features such as user behavior, social models, and user-generated content to highlight the most notable research trends and application systems built over such appealing models and online media data. It first describes the most popular social networks by analyzing the growth trend, the user behaviors, the evolution of social groups and models, and the most relevant types of data continuously generated and updated by the users. Next, the most recent and valuable applications of data mining techniques to social network models and user-generated content are presented. Discussed works address both social model extractions tailored to semantic knowledge inference and automatic understanding of the user-generated content. Finally, prospects of data mining research on social networks are provided as well.

Competence Management over Social Networks Through Dynamic Taxonomies

2013 ◽  
pp. 103-120
Author(s):  
Giuseppe Berio ◽  
Antonio Di Leva ◽  
Mounira Harzallah ◽  
Giovanni M. Sacco
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Real World ◽  
Reference Model ◽  
Effective Solution ◽  
Network Information ◽  
The Social ◽  
Competence Management

The exploitation and integration of social network information in a competence reference model (CRAI, Competence, Resource, Aspect, Individual) are discussed. The Social-CRAI model, which extends CRAI to social networks, provides an effective solution to this problem and is discussed in detail. Finally, dynamic taxonomies, a model supporting explorative conceptual search, are introduced and their use in the context of the Social-CRAI model for exploring retrieved information available in social networks is discussed. A real-world example is provided.

Unraveling the Taste Fabric of Social Networks

2009 ◽  
pp. 1521-1546
Author(s):  
Hugo Liu ◽  
Pattie Maes ◽  
Glorianna Davenport
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Social Connectedness ◽  
Natural Language Text ◽  
Cross Domain ◽  
Semantic Mining ◽  
The Rich ◽  
Language Text

Popular online social networks such as Friendster and MySpace do more than simply reveal the superficial structure of social connectedness—the rich meanings bottled within social network profiles themselves imply deeper patterns of culture and taste. If these latent semantic fabrics of taste could be harvested formally, the resultant resource would afford completely novel ways for representing and reasoning about web users and people in general. This paper narrates the theory and technique of such a feat—the natural language text of 100,000 social network profiles were captured, mapped into a diverse ontology of music, books, films, foods, etc., and machine learning was applied to infer a semantic fabric of taste. Taste fabrics bring us closer to improvisational manipulations of meaning, and afford us at least three semantic functions—the creation of semantically flexible user representations, cross-domain taste-based recommendation, and the computation of taste-similarity between people— whose use cases are demonstrated within the context of three applications—the InterestMap, Ambient Semantics, and IdentityMirror. Finally, we evaluate the quality of the taste fabrics, and distill from this research reusable methodologies and techniques of consequence to the semantic mining and Semantic Web communities.

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