Personalized Recommendation in Dynamic and Multidimensional Social Network

2011 ◽  
pp. 75-78
Keyword(s):  
Social Network ◽  
Personalized Recommendation

scholarly journals I Know Where You Are Coming From: On the Impact of Social Media Sources on AI Model Performance (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13971-13972
Author(s):  
Yang Qi ◽  
Farseev Aleksandr ◽  
Filchenkov Andrey
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Model Performance ◽  
User Profiling ◽  
Personalized Recommendation ◽  
Modal Data ◽  
Social Media Networks ◽  
The Impact

Nowadays, social networks play a crucial role in human everyday life and no longer purely associated with spare time spending. In fact, instant communication with friends and colleagues has become an essential component of our daily interaction giving a raise of multiple new social network types emergence. By participating in such networks, individuals generate a multitude of data points that describe their activities from different perspectives and, for example, can be further used for applications such as personalized recommendation or user profiling. However, the impact of the different social media networks on machine learning model performance has not been studied comprehensively yet. Particularly, the literature on modeling multi-modal data from multiple social networks is relatively sparse, which had inspired us to take a deeper dive into the topic in this preliminary study. Specifically, in this work, we will study the performance of different machine learning models when being learned on multi-modal data from different social networks. Our initial experimental results reveal that social network choice impacts the performance and the proper selection of data source is crucial.

scholarly journals Research on Relationship Strength under Personalized Recommendation Service

2020 ◽  
Vol 12 (4) ◽  
pp. 1459 ◽  
Author(s):  
Wanqiong Tao ◽  
Chunhua Ju ◽  
Chonghuan Xu
Keyword(s):  
Social Network ◽  
Online Social Network ◽  
Interaction Strength ◽  
Experimental Results ◽  
Strength Calculation ◽  
Personalized Recommendation ◽  
Relationship Strength ◽  
Interactive Behavior ◽  
Relationship Of ◽  
Feature Attribute

Relationship of users in an online social network can be applied to promote personalized recommendation services. The measurement of relationship strength between user pairs is crucial to analyze the user relationship, which has been developed by many methods. An issue that has not been fully addressed is that the interaction behavior of individuals subjected to the activity field preference and interactive habits will affect interactive behavior. In this paper, the three-way representation of the activity field is given firstly, the contribution weight of the activity filed preferences is measured based on the interactions in the positive and boundary regions. Then, the interaction strength is calculated, integrating the contribution weight of the activity field preference and interactive habit. Finally, user relationship strength is calculated by fusing the interaction strength, common friend rate and similarity of feature attribute. The experimental results show that the proposed method can effectively improve the accuracy of relationship strength calculation.

Applying Personalized Recommendation for Social Network Marketing

E-Marketing ◽  
2012 ◽  
pp. 137-150 ◽  
Author(s):  
Leila Esmaeili ◽  
Ramin Nasiri ◽  
Behrouz Minaei-Bidgoli
Keyword(s):  
Social Network ◽  
Business Models ◽  
Online Social Network ◽  
Age Groups ◽  
User Preferences ◽  
Ease Of Use ◽  
Personalized Recommendation ◽  
Network Marketing ◽  
New Business ◽  
Using Data

The competition among manufacturers and service providing companies as well as the widespread presence of electronic processes has introduced new business models that need special e-Marketing. Social network marketing is one of the most recent types of marketing. Today, due to their flexibility and ease of use, social networks have fallen in the center of attention for users of various age groups. The variety of online social network groups, some of which are created with commercial goals, has made users uncertain and skeptical; on the other hand, in today’s competitive market, companies are seeking their potential and actual customers. To solve this problem, this paper introduced a group recommender system which, using data mining techniques and information theory, offers customized recommendations based on user preferences. Supposing that users in each group share similar characteristics, heterogeneous members are identified and removed. Unlike other methods, in special cases where the user does not have relationships with other members or when an activity history for the user does not exist, this method could yet offer recommendations.

Personalized Recommendation Research in E-Learning Systems

2013 ◽  
Vol 433-435 ◽  
pp. 603-606
Author(s):  
Bing Wu ◽  
Ping Ping Chen
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Learning Systems ◽  
Learning System ◽  
Personalized Recommendation ◽  
Important Research ◽  
Research Areas ◽  
Communication Efficiency ◽  
E Learning ◽  
Explicit Study

The purpose of this paper is to review the literatures which have made an explicit study on personalized recommendation in E-Learning systems. By identifying the important research areas, which are in different perspectives, firstly, filtering recommendation is introduced before the illustration of how it has been developed in E-Learning systems. Then personalized recommendation is proposed for E-Learning system. Although social network is the basic way to improve the communication efficiency with others in E-Learning system, previous studies pay less attention on this. Therefore social network analysis should be taken into consideration for the recommendation in E-Learning system for further research.

scholarly journals Evolutionary Prediction of Nonstationary Event Popularity Dynamics of Weibo Social Network Using Time-Series Characteristics

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Xiaoliang Chen ◽  
Xiang Lan ◽  
Jihong Wan ◽  
Peng Lu ◽  
Ming Yang
Keyword(s):  
Time Series ◽  
Social Network ◽  
Prediction Model ◽  
Linear Models ◽  
Personalized Recommendation ◽  
Popularity Dynamics ◽  
Social Media Platforms ◽  
Popularity Prediction ◽  
Prediction Approach ◽  
Prediction Problems

A growing number of web users around the world have started to post their opinions on social media platforms and offer them for share. Building a highly scalable evolution prediction model by means of evolution trend volatility plays a significant role in the operations of enterprise marketing, public opinion supervision, personalized recommendation, and so forth. However, the historical patterns cannot cover the systematical time-series dynamic and volatility features in the prediction problems of a social network. This paper aims to investigate the popularity prediction problem from a time-series perspective utilizing dynamic linear models. First, the stationary and nonstationary time series of Weibo hot events are detected and transformed into time-dependent variables. Second, a systematic general popularity prediction model N- SEP 2 M is proposed to recognize and predict the nonstationary event propagation of a hot event on the Weibo social network. Third, the explanatory compensation variable social intensity (SI) is introduced to optimize the model N- SEP 2 M. Experiments on three Weibo hot events with different subject classifications show that our prediction approach is effective for the propagation of hot events with burst traffic.

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