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.

Applying Personalized Recommendation for Social Network Marketing

2012 ◽  
Vol 2 (1) ◽  
pp. 50-63
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 ◽  
Special Cases ◽  
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.

Sentiment analysis of foreign tourists to Bangkok using data mining through online social network

2017 ◽  
Author(s):  
Taweesak Kuhamanee ◽  
Nattaphon Talmongkol ◽  
Krit Chaisuriyakul ◽  
Wimol San-Um ◽  
Noppadon Pongpisuttinun ◽  
...  
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Sentiment Analysis ◽  
Online Social Network ◽  
Using Data

scholarly journals Adaptive Data Placement for Improving Performance of Online Social Network Services in a Multicloud Environment

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Seunghee Han ◽  
Bosung Kim ◽  
Jaemin Han ◽  
Kyehee Kim ◽  
JooSeok Song
Keyword(s):  
Social Network ◽  
Online Social Network ◽  
Data Placement ◽  
Network Services ◽  
Resource Contention ◽  
Redundant Data ◽  
User Data ◽  
Placement Algorithm ◽  
Using Data ◽  
Resource Status

The existing online social network (OSN) services in a multiple-cloud (Multicloud) environment use replications to store user data for improving the service performance. However, it not only generates tremendous traffic for synchronization between data but also stores considerable redundant data, thus causing large storage costs. In addition, it does not provide dynamic load balancing considering the resource status of each cloud. As a result, it cannot cope with the degradation of performance caused by the resource contention. We introduce an adaptive data placement algorithm without the replications for improving the performance of the OSN services in the Multicloud environment. Our approach is designed to avoid server overhead using data balancing technique, which locates data from a cloud to another according to the amount of traffic. To provide acceptable latency delay, it also considers the relationship between users and the distance between user and cloud when transferring data. To validate our approach, we experimented with actual users’ locations and times of use collected from OSN services. Our findings indicate that this approach can reduce the resource contention by an average of more than 59%, reduce storage volume to at least 50%, and maintain the latency delay under 50 ms.

scholarly journals Online social network relationships influenced on a retweeting

2020 ◽  
Vol 20 (2) ◽  
pp. 1037
Author(s):  
Iman K. Abbood ◽  
Saad Talib Hasson
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Correlation Coefficient ◽  
Social Relationship ◽  
Online Social Network ◽  
Ease Of Use ◽  
Breaking News ◽  
Main Effect ◽  
Promising Source

<p>Social network users spending a lot of time to post, search, interact and read the news on blogging platforms. In this era, social media is becoming a suitable place for discovering and exchanging new updates. However, Common social media helps the user to share his news online by a one-click. The ease-of-use leads to present novel breaking news to show up first on micro blogs. Twitter is one of the well-known micro blogging platforms with more than 250 million users, in which retweeting is a manageable way to share and sawing news. It is significant to foretell the retweeting and influence in a social relationship. The Correlation Coefficient formula has been used to determine the level of correlation between a user and his retweeters (followers, friends, and strangers) in social networks. Such correlation can be reached by utilizing the collected user information on Twitter with some features which have a main effect on retweet behavior. In this study, the focus is on particular friends, followers, and a retweet to be the promising source of relationships between users of social media. Experimental results based on twitter dataset showed that the Correlation Coefficient formula can be used as a predicting model, and it is a general framework to gain better fulfillment in calculating the correlation between the user, friends, and followers in social networks..  Their influence on the accuracy in predicting a retweet is also accomplished.</p>

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.

A Layered Parameterized Framework for Intelligent Information Retrieval in Dynamic Social Network using Data Mining

2014 ◽  
pp. 231-248
Author(s):  
Shailendra Kumar Sonkar ◽  
Vishal Bhatnagar ◽  
Rama Krishna Challa
Keyword(s):  
Data Mining ◽  
Information Retrieval ◽  
Social Network ◽  
Age Groups ◽  
Dynamic Social Network ◽  
Intelligent Information Retrieval ◽  
Dynamic Social Networks ◽  
Intelligent Information ◽  
Using Data ◽  
Key Parameter

Dynamic social networks contain vast amounts of data, which is changing continuously. A search in a dynamic social network does not guarantee relevant, filtered, and timely information to the users all the time. There should be some sequential processes to apply some techniques and store the information internally that provides the relevant, filtered, and timely information to the users. In this chapter, the authors categorize the social network users into different age groups and identify the suitable and appropriate parameters, then assign these parameters to the already categorized age groups and propose a layered parameterized framework for intelligent information retrieval in dynamic social network using different techniques of data mining. The primary data mining techniques like clustering group the different groups of social network users based on similarities between key parameter items and by classifying the different classes of social network users based on differences among key parameter items, and it can be association rule mining, which finds the frequent social network users from the available users.

Business Models for On-Line Social Networks

2013 ◽  
pp. 163-174
Author(s):  
Omer F. Rana ◽  
Simon Caton
Keyword(s):  
Social Networks ◽  
Cloud Computing ◽  
Social Network ◽  
Real World ◽  
Virtual Communities ◽  
Business Models ◽  
New Business ◽  
On Line ◽  
Share Information ◽  
New Business Models

With the increasingly ubiquitous nature of social networks and Cloud computing, users are starting to explore new ways to interact with and exploit these developing paradigms. Social networks reflect real world relationships that allow users to share information and form connections, essentially creating dynamic virtual communities. By leveraging the pre-established trust formed through friend relationships within a social network “Social Clouds” can be realized, which enable friends to share resources within the context of a social network. The creation of Social Clouds gives rise to new business models through collaboration within social networks. In this paper, the authors describe such business models and discuss their impact.

Business Models for On-line Social Networks

2012 ◽  
pp. 1501-1512 ◽  
Author(s):  
Omer F. Rana ◽  
Simon Caton
Keyword(s):  
Social Networks ◽  
Cloud Computing ◽  
Social Network ◽  
Real World ◽  
Virtual Communities ◽  
Business Models ◽  
New Business ◽  
On Line ◽  
Share Information ◽  
New Business Models

With the increasingly ubiquitous nature of social networks and Cloud computing, users are starting to explore new ways to interact with and exploit these developing paradigms. Social networks reflect real world relationships that allow users to share information and form connections, essentially creating dynamic virtual communities. By leveraging the pre-established trust formed through friend relationships within a social network “Social Clouds” can be realized, which enable friends to share resources within the context of a social network. The creation of Social Clouds gives rise to new business models through collaboration within social networks. In this paper, the authors describe such business models and discuss their impact.

Business Models for On-line Social Networks

2010 ◽  
Vol 2 (3) ◽  
pp. 31-41 ◽  
Author(s):  
Omer F. Rana ◽  
Simon Caton
Keyword(s):  
Social Networks ◽  
Cloud Computing ◽  
Social Network ◽  
Real World ◽  
Virtual Communities ◽  
Business Models ◽  
New Business ◽  
On Line ◽  
Share Information ◽  
New Business Models

With the increasingly ubiquitous nature of social networks and Cloud computing, users are starting to explore new ways to interact with and exploit these developing paradigms. Social networks reflect real world relationships that allow users to share information and form connections, essentially creating dynamic virtual communities. By leveraging the pre-established trust formed through friend relationships within a social network “Social Clouds” can be realized, which enable friends to share resources within the context of a social network. The creation of Social Clouds gives rise to new business models through collaboration within social networks. In this paper, the authors describe such business models and discuss their impact.

A Layered Parameterized Framework for Intelligent Information Retrieval in Dynamic Social Network using Data Mining

2016 ◽  
pp. 458-475
Author(s):  
Shailendra Kumar Sonkar ◽  
Vishal Bhatnagar ◽  
Rama Krishna Challa
Keyword(s):  
Data Mining ◽  
Information Retrieval ◽  
Social Network ◽  
Age Groups ◽  
Dynamic Social Network ◽  
Intelligent Information Retrieval ◽  
Dynamic Social Networks ◽  
Intelligent Information ◽  
Using Data ◽  
Key Parameter

Dynamic social networks contain vast amounts of data, which is changing continuously. A search in a dynamic social network does not guarantee relevant, filtered, and timely information to the users all the time. There should be some sequential processes to apply some techniques and store the information internally that provides the relevant, filtered, and timely information to the users. In this chapter, the authors categorize the social network users into different age groups and identify the suitable and appropriate parameters, then assign these parameters to the already categorized age groups and propose a layered parameterized framework for intelligent information retrieval in dynamic social network using different techniques of data mining. The primary data mining techniques like clustering group the different groups of social network users based on similarities between key parameter items and by classifying the different classes of social network users based on differences among key parameter items, and it can be association rule mining, which finds the frequent social network users from the available users.

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