Recommender Systems for the Social Networking Context for Collaborative Filtering and Content-Based Approaches

2021 ◽  
pp. 121-137
Author(s):  
R.S.M. Lakshmi Patibandla ◽  
V. Lakshman Narayana ◽  
Arepalli Peda Gopi ◽  
B. Tarakeswara Rao
Keyword(s):  
Social Networking ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
The Social

scholarly journals Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems

2020 ◽  
Vol 10 (4) ◽  
pp. 1257 ◽  
Author(s):  
Liang Zhang ◽  
Quanshen Wei ◽  
Lei Zhang ◽  
Baojiao Wang ◽  
Wen-Hsien Ho
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Social Relationships ◽  
Two Stage ◽  
Proposed Model ◽  
The Social ◽  
Item List ◽  
Ranking Strategy ◽  
High Prediction ◽  
Optimization Mechanism

Conventional recommender systems are designed to achieve high prediction accuracy by recommending items expected to be the most relevant and interesting to users. Therefore, they tend to recommend only the most popular items. Studies agree that diversity of recommendations is as important as accuracy because it improves the customer experience by reducing monotony. However, increasing diversity reduces accuracy. Thus, a recommendation algorithm is needed to recommend less popular items while maintaining acceptable accuracy. This work proposes a two-stage collaborative filtering optimization mechanism that obtains a complete and diversified item list. The first stage of the model incorporates multiple interests to optimize neighbor selection. In addition to using conventional collaborative filtering to predict ratings by exploiting available ratings, the proposed model further considers the social relationships of the user. A novel ranking strategy is then used to rearrange the list of top-N items while maintaining accuracy by (1) rearranging the area controlled by the threshold and by (2) maximizing popularity while maintaining an acceptable reduction in accuracy. An extensive experimental evaluation performed in a real-world dataset confirmed that, for a given loss of accuracy, the proposed model achieves higher diversity compared to conventional approaches.

Toward Social-Semantic Recommender Systems

2016 ◽  
Vol 7 (1) ◽  
pp. 1-30 ◽  
Author(s):  
Dalia Sulieman ◽  
Maria Malek ◽  
Hubert Kadima ◽  
Dominique Laurent
Keyword(s):  
Social Network ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Semantic Information ◽  
Basic Problem ◽  
Real Data ◽  
Data Sets ◽  
The Social ◽  
Recommendation Algorithms ◽  
Hybrid Recommendation

In this article, the authors consider the basic problem of recommender systems that is identifying a set of users to whom a given item is to be recommended. In practice recommender systems are run against huge sets of users, and the problem is then to avoid scanning the whole user set in order to produce the recommendation list. To cope with problem, they consider that users are connected through a social network and that taxonomy over the items has been defined. These two kinds of information are respectively called social and semantic information. In their contribution the authors suggest combining social information with semantic information in one algorithm in order to compute recommendation lists by visiting a limited part of the social network. In their experiments, the authors use two real data sets, namely Amazon.com and MovieLens, and they compare their algorithms with the standard item-based collaborative filtering and hybrid recommendation algorithms. The results show satisfying accuracy values and a very significant improvement of performance, by exploring a small part of the graph instead of exploring the whole graph.

Improving Context Aware Recommendation Performance by Using Social Networks

2015 ◽  
Vol 8 (3) ◽  
pp. 73-87
Author(s):  
Golshan Assadat Afzali Boroujeni ◽  
Seyed Alireza Hashemi Golpayegani
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
High Volume ◽  
Context Aware ◽  
Mobile Data ◽  
The People ◽  
Proposed Model ◽  
The Social ◽  
Current Context

Ecommerce systems employ recommender systems to enhance the customer loyalty and hence increasing the cross-selling of products. In collaborative filtering - as the most popular method in recommender systems - an implicit network is formed among all the people. In any network, there are some individuals who have some inspirational power over the others leading them to influence their decisions and behaviours. But it seems that these methods do not support context awareness in mobile commerce environments. Furthermore, they lack high accuracy and also require high volume of computations due to not distinguish between neighbours as a friend or a stranger. This paper proposes a new model for recommender systems which are based on mobile data. This model uses these data to extract current users' context and also to identify individuals with the highest influence. Then, the system uses the information of these identified impressive users in the current context existed in the social networks for making recommendations. Beside of achieving higher accuracy, the proposed model has resolved cold start problem in collaborative filtering systems.

Improving Context Aware Recommendation Performance by Using Social Networks

2014 ◽  
Vol 7 (3) ◽  
pp. 1-14
Author(s):  
Golshan Assadat Afzali Boroujeni ◽  
Seyed Alireza Hashemi Golpayegani
Keyword(s):  
Social Networks ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
High Volume ◽  
Context Aware ◽  
Mobile Data ◽  
The People ◽  
Proposed Model ◽  
The Social ◽  
Current Context

Ecommerce systems employ recommender systems to enhance the customer loyalty and hence increasing the cross-selling of products. In collaborative filtering—as the most popular method in recommender systems—an implicit network is formed among all the people. In any network, there are some individuals who have some inspirational power over the others leading them to influence their decisions and behaviours. But it seems that these methods do not support context awareness in mobile commerce environments. Furthermore, they lack high accuracy and also require high volume of computations due to not distinguish between neighbours as a friend or a stranger. This paper proposes a new model for recommender systems which are based on mobile data. This model uses these data to extract current users' context and also to identify individuals with the highest influence. Then, the system uses the information of these identified impressive users in the current context existed in the social networks for making recommendations. Beside of achieving higher accuracy, the proposed model has resolved cold start problem in collaborative filtering systems.

Performance Comparison among Meta Classifier Algorithms in WEKA

2019 ◽  
Vol 1 (2) ◽  
pp. 106-112
Author(s):  
Nur Amiratun Nazihah Roslan ◽  
Hairulnizam Mahdin ◽  
Shahreen Kasim
Keyword(s):  
Social Networking ◽  
Performance Comparison ◽  
Data Availability ◽  
Data Mining Tool ◽  
Huge Data ◽  
Technology Advancement ◽  
The Social ◽  
The World ◽  
Media Platform ◽  
Mining Tool

With the rise of social networking approach, there has been a surge of users generated content all over the world and with that in an era where technology advancement are up to the level where it could put us in a step ahead of pathogens and germination of diseases, we couldn’t help but to take advantage of that advancement and provide an early precaution measures to overcome it. Twitter on the other hand are one of the social media platform that provides access towards a huge data availability. To manipulate those data and transform it into an important information that could be used in many different scope that could help improve people’s life for the better. In this paper, we gather all algorithm that are available inside Meta Classifier to compare between them on which algorithm suited the most with the dengue fever dataset. This research are using WEKA as the data mining tool for data analyzation.

scholarly journals The Perceived Impact of Social Networking Sites and Apps on the Social Capital of Saudi Postgraduate Students: A Case Study

2021 ◽  
Vol 13 (1) ◽  
pp. 20
Author(s):  
Abdulelah A. Alghamdi ◽  
Margaret Plunkett
Keyword(s):  
Social Capital ◽  
Life Satisfaction ◽  
Social Networking ◽  
Social Networking Sites ◽  
Civic Participation ◽  
Social Trust ◽  
Political Engagement ◽  
Postgraduate Students ◽  
The Social ◽  
The Impact

With the increased use of Social Networking Sites and Apps (SNSAs) in Saudi Arabia, it is important to consider the impact of this on the social lives of tertiary students, who are heavy users of such technology. A mixed methods study exploring the effect of SNSAs use on the social capital of Saudi postgraduate students was conducted using a multidimensional construct of social capital, which included the components of life satisfaction, social trust, civic participation, and political engagement. Data were collected through surveys and interviews involving 313 male and 293 female postgraduate students from Umm Al-Qura University (UQU) in Makkah. Findings show that male and female participants perceived SNSAs use impacting all components of social capital at a moderate and mainly positive level. Correlational analysis demonstrated medium to large positive correlations among components of social capital. Gender differences were not evident in the life satisfaction and social trust components; however, females reported more involvement with SNSAs for the purposes of political engagement while males reported more use for civic participation, which is an interesting finding, in light of the norms and traditional culture of Saudi society.

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