scholarly journals A Prediction Method of Mobile User Preference Based on the Influence between Users

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yancui Shi ◽  
Jianhua Cao ◽  
Congcong Xiong ◽  
Xiankun Zhang
Keyword(s):  
Social Network ◽  
User Behavior ◽  
Prediction Method ◽  
Mobile User ◽  
User Preferences ◽  
User Preference ◽  
Context Information ◽  
Mobile Social Network ◽  
Data Set ◽  
Interaction Behavior

User preference will be impacted by other users. To accurately predict mobile user preference, the influence between users is introduced into the prediction model of user preference. First, the mobile social network is constructed according to the interaction behavior of the mobile user, and the influence of the user is calculated according to the topology of the constructed mobile social network and mobile user behavior. Second, the influence between users is calculated according to the user’s influence, the interaction behavior between users, and the similarity of user preferences. When calculating the influence based on the interaction behavior, the context information is considered; the context information and the order of user preferences are considered when calculating the influence based on the similarity of user preferences. The improved collaborative filtering method is then employed to predict mobile user preferences based on the obtained influence between users. Finally, the experiment is executed on the real data set and the integrated data set, and the results show that the proposed method can obtain more accurate mobile user preferences than those of existing methods.

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 WHY MAKING MATTERS ONLINE: THE PINTEREST-DIY DATA SET

2021 ◽  
Author(s):  
Michelle Sylvia Weintraub ◽  
David R W Sears
Keyword(s):  
User Behavior ◽  
Real Life ◽  
User Preferences ◽  
Social Networking Service ◽  
Text Data ◽  
Data Set ◽  
Creative Processes ◽  
User Comments ◽  
Lead Users ◽  
Do It Yourself

ABSTRACT The Do-It-Yourself (DIY) community is currently one of the largest creative content communities on Pinterest (Hall et al., 2018), a social networking service (SNS) that encourages users to both share information about creative processes and attempt projects in real life (IRL). Pinterest users share ongoing projects by creating Project “Pins”, which consist of images, videos, and text descriptions of creative content. And yet, while several studies have investigated user behavior in relation to everyday ideation and creativity on the site (Linder et al., 2014, Hu et al., 2018, Mull and Lee, 2014), little is known about the characteristics that lead users to prefer some DIY projects over others. Thus, this paper introduces the Pinterest-DIY data set, which consists of text data mined from 500 DIY project Pins on Pinterest. Using a custom sampling approach, we created a taxonomy of DIY characteristics related to each Pin’s project type, function, materials, and complexity. To measure user preferences on the site, we also conducted a sentiment analysis on user comments for each DIY project Pin. This paper introduces the data set and presents two use cases for the internet research community using both exploratory and confirmatory statistical methods. In our view, the Pinterest-DIY data set will provide further opportunities to examine whether, and to what degree, participation in online DIY communities promotes everyday creativity and increases engagement with physical matter.

scholarly journals Deep Reinforcement Learning Based on Link Prediction Method in Social Network Analysis

2019 ◽  
Vol 9 (2S2) ◽  
pp. 820-826
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Reinforcement Learning ◽  
Supervised Learning ◽  
Link Prediction ◽  
Prediction Models ◽  
Learning Strategy ◽  
Prediction Method ◽  
Gradient Boosting ◽  
Data Set

Improving the performance of link prediction is a significant role in the evaluation of social network. Link prediction is known as one of the primary purposes for recommended systems, bio information, and web. Most machine learning methods that depend on SNA model’s metrics use supervised learning to develop link prediction models. Supervised learning actually needed huge amount of data set to train the model of link prediction to obtain an optimal level of performance. In few years, Deep Reinforcement Learning (DRL) has achieved excellent success in various domain such as SNA. In this paper, we present the use of deep reinforcement learning (DRL) to improve the performance and accuracy of the model for the applied dataset. The experiment shows that the dataset created by the DRL model through self-play or auto-simulation can be utilized to improve the link prediction model. We have used three different datasets: JUNANES, MAMBO, JAKE. Experimental results show that the DRL proposed method provide accuracy of 85% for JUNANES, 87% for MAMABO, and 78% for JAKE dataset which outperforms the GBM next highest accuracy of 75% for JUNANES, 79% for MAMBO and 71% for JAKE dataset respectively trained with 2500 iteration and also in terms of AUC measures as well. The DRL model shows the better efficiency than a traditional machine learning strategy, such as, Random Forest and the gradient boosting machine (GBM).

Constructing the social network prediction model based on data mining and link prediction analysis

2019 ◽  
Vol 38 (2) ◽  
pp. 320-333
Author(s):  
Yuxian Gao
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Link Prediction ◽  
Prediction Method ◽  
Analysis Method ◽  
Data Set ◽  
Content Type ◽  
Community Mining ◽  
The Relationship

Purpose The purpose of this paper is to apply link prediction to community mining and to clarify the role of link prediction in improving the performance of social network analysis. Design/methodology/approach In this study, the 2009 version of Enron e-mail data set provided by Carnegie Mellon University was selected as the research object first, and bibliometric analysis method and citation analysis method were adopted to compare the differences between various studies. Second, based on the impact of various interpersonal relationships, the link model was adopted to analyze the relationship among people. Finally, the factorization of the matrix was further adopted to obtain the characteristics of the research object, so as to predict the unknown relationship. Findings The experimental results show that the prediction results obtained by considering multiple relationships are more accurate than those obtained by considering only one relationship. Research limitations/implications Due to the limited number of objects in the data set, the link prediction method has not been tested on the large-scale data set, and the validity and correctness of the method need to be further verified with larger data. In addition, the research on algorithm complexity and algorithm optimization, including the storage of sparse matrix, also need to be further studied. At the same time, in the case of extremely sparse data, the accuracy of the link prediction method will decline a lot, and further research and discussion should be carried out on the sparse data. Practical implications The focus of this research is on link prediction in social network analysis. The traditional prediction model is based on a certain relationship between the objects to predict and analyze, but in real life, the relationship between people is diverse, and different relationships are interactive. Therefore, in this study, the graph model is used to express different kinds of relations, and the influence between different kinds of relations is considered in the actual prediction process. Finally, experiments on real data sets prove the effectiveness and accuracy of this method. In addition, link prediction, as an important part of social network analysis, is also of great significance for other applications of social network analysis. This study attempts to prove that link prediction is helpful to the improvement of performance analysis of social network by applying link prediction to community mining. Originality/value This study adopts a variety of methods, such as link prediction, data mining, literature analysis and citation analysis. The research direction is relatively new, and the experimental results obtained have a certain degree of credibility, which is of certain reference value for the following related research.

scholarly journals Collaborative prediction of web service quality based on user preferences and services

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242089
Author(s):  
Yang Song
Keyword(s):  
Quality Of Service ◽  
Service Quality ◽  
Collaborative Filtering ◽  
Web Service ◽  
Prediction Method ◽  
User Preferences ◽  
User Preference ◽  
Different Types ◽  
Collaborative Prediction

The prediction of web service quality plays an important role in improving user services; it has been one of the most popular topics in the field of Internet services. In traditional collaborative filtering methods, differences in the personalization and preferences of different users have been ignored. In this paper, we propose a prediction method for web service quality based on different types of quality of service (QoS) attributes. Different extraction rules are applied to extract the user preference matrices from the original web data, and the negative value filtering-based top-K method is used to merge the optimization results into the collaborative prediction method. Thus, the individualized differences are fully exploited, and the problem of inconsistent QoS values is resolved. The experimental results demonstrate the validity of the proposed method. Compared with other methods, the proposed method performs better, and the results are closer to the real values.

scholarly journals Optimal breeding-value prediction using a Sparse Selection Index

Genetics ◽  
2021 ◽  
Author(s):  
Marco Lopez-Cruz ◽  
Gustavo de los Campos
Keyword(s):  
Sample Size ◽  
Dna Sequences ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Regularization Parameter ◽  
Selection Index ◽  
Prediction Method ◽  
Training Data ◽  
Breeding Value ◽  
Data Set

Abstract Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and in linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a Sparse Selection Index (SSI) that integrates Selection Index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-BLUP (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in ten different environments) that the SSI can achieve significant (anywhere between 5-10%) gains in prediction accuracy relative to the G-BLUP.

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