scholarly journals Investigating the Temporal Effect of User Preferences with Application in Movie Recommendation

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Wen-Jun Li ◽  
Qiang Dong ◽  
Yan Fu
Keyword(s):  
Rapid Development ◽  
User Preferences ◽  
Mobile Internet ◽  
Smart Devices ◽  
Data Sets ◽  
Rating Data ◽  
Temporal Effect ◽  
Proposed Model ◽  
Target User ◽  
The Difference

As the rapid development of mobile Internet and smart devices, more and more online content providers begin to collect the preferences of their customers through various apps on mobile devices. These preferences could be largely reflected by the ratings on the online items with explicit scores. Both of positive and negative ratings are helpful for recommender systems to provide relevant items to a target user. Based on the empirical analysis of three real-world movie-rating data sets, we observe that users’ rating criterions change over time, and past positive and negative ratings have different influences on users’ future preferences. Given this, we propose a recommendation model on a session-based temporal graph, considering the difference of long- and short-term preferences, and the different temporal effect of positive and negative ratings. The extensive experiment results validate the significant accuracy improvement of our proposed model compared with the state-of-the-art methods.

scholarly journals A Novel Active Contours Model for Environmental Change Detection from Multitemporal Synthetic Aperture Radar Images

2020 ◽  
Vol 12 (11) ◽  
pp. 1746
Author(s):  
Salman Ahmadi ◽  
Saeid Homayouni
Keyword(s):  
Synthetic Aperture Radar ◽  
Change Detection ◽  
Active Contours ◽  
Training Data ◽  
Synthetic Aperture ◽  
Data Sets ◽  
Difference Image ◽  
Proposed Model ◽  
The Difference ◽  
Aperture Radar

In this paper, we propose a novel approach based on the active contours model for change detection from synthetic aperture radar (SAR) images. In order to increase the accuracy of the proposed approach, a new operator was introduced to generate a difference image from the before and after change images. Then, a new model of active contours was developed for accurately detecting changed regions from the difference image. The proposed model extracts the changed areas as a target feature from the difference image based on training data from changed and unchanged regions. In this research, we used the Otsu histogram thresholding method to produce the training data automatically. In addition, the training data were updated in the process of minimizing the energy function of the model. To evaluate the accuracy of the model, we applied the proposed method to three benchmark SAR data sets. The proposed model obtains 84.65%, 87.07%, and 96.26% of the Kappa coefficient for Yellow River Estuary, Bern, and Ottawa sample data sets, respectively. These results demonstrated the effectiveness of the proposed approach compared to other methods. Another advantage of the proposed model is its high speed in comparison to the conventional methods.

scholarly journals Media Leaders Mining Based on Android News Apps

2019 ◽  
Vol 2 (1) ◽  
pp. 4
Author(s):  
Sijia Wang ◽  
Miao Zhang
Keyword(s):  
New Media ◽  
Rapid Development ◽  
Experimental Studies ◽  
Mobile Internet ◽  
Major Influence ◽  
Data Sets ◽  
The Public ◽  
Spread Of Influence ◽  
Rapid Dissemination ◽  
Media Reports

<p align="justify">With the rapid development of the mobile Internet, the mobile news apps have become the most important way for the public to obtain news. As a new media carrier and communication platform,the mobile news apps can promote the rapid dissemination of information and the rapid spread of influence.  Some media have a major influence  on the direction of other media reports and the behavioral decisions of the public. These media can be regarded as media leaders. Media leaders are very important in the dissemination of news. By identifying media leaders, companies or governments can promote sales or guide public opinion separately. This article believes that media leaders mainly achieve their own influence by publishing news, so this article uses the news published by the mobile news apps as an entry point. This paper firstly solves the problem of data crawling in mobile news apps, and proposes a data crawling method based on reverse analysis, and obtains the data source. Then, reconstruct the reprinting path of the news, and carry out accurate traceability. Finally, cluster the news based on LDA, and propose an algorithm for mining media leaders from three aspects: influence, activity and preference. Experimental studies of data sets have shown that our algorithms can effectively identify media leaders.</p>

scholarly journals CFDIL: A Context-aware Feature Deep Interaction Learning for  App Recommendation

2021 ◽  
Author(s):  
Qingbo Hao ◽  
Ke Zhu ◽  
Chundong Wang ◽  
Peng Wang ◽  
Xiuliang Mo ◽  
...  
Keyword(s):  
Mobile Applications ◽  
Historical Data ◽  
Contextual Information ◽  
Rapid Development ◽  
User Preferences ◽  
Mobile Internet ◽  
Context Aware ◽  
Interaction Data ◽  
Learning Potential ◽  
The One

Abstract The rapid development of Mobile Internet has spa-wned various mobile applications (apps). A large number of apps make it difficult for users to choose apps conveniently, causing the app overload problem. As the most effective tool to solve the problem of app overload, the app recommendation has attracted extensive attention of researchers. Traditional recommendation methods usually use historical data of apps used by users to explore their preferences, and then make an app recommendation list for users. Although the traditional app recommendation methods have achieved certain results, the performance of app recommendation still needs to be improved due to the following two reasons. On the one hand, it is difficult to construct traditional app recommendation models when facing with the sparse user-app interaction data. On the other hand, contextual information has a large impact on users’ app usage preferences, which is often overlooked by traditional app recommendation methods. To overcome the aforementioned problems, we proposed a Context-aware Feature Deep Interaction Learning (CFDIL) method to explore user preferences, and then perform app recommendation by learning potential user-app relationships in different contexts. The novelty of CFDIL is as follows: (1) CFDIL incorporates contextual features into users' preferences modeling by constructing a novel user and app feature portrait. (2) The problem of data sparsity is effectively solved by the use of dense user and app feature portraits, as well as the tensor operations for label sets. (3) CFDIL trains a new deep network structure, which can make accurate app recommendation using the contextual information and attribute information of users and apps. We applied CFDIL on three real datasets and conducted extensive experiments, which showed that CFDIL outperformed the benchmark method.

Blog Recommendation and Management Implications in an Emergency Context: An Information Entropy Perspective

2017 ◽  
Vol 34 (01) ◽  
pp. 1740007 ◽  
Author(s):  
Siqing Shan ◽  
Jihong Shi ◽  
Qi Yan
Keyword(s):  
Decision Making ◽  
Information Entropy ◽  
Rapid Development ◽  
Mobile Internet ◽  
User Generated Content ◽  
Management Implications ◽  
Modeling Methodology ◽  
Proposed Model ◽  
Content Similarity ◽  
Primary Contribution

A modeling methodology for blog recommendation and forecasting based on information entropy is presented. With the increasing popularity of smartphones and the rapid development of the mobile Internet, the amount of user-generated content such as blogs is increasing daily. Valuable information, such as bloggers’ opinions, feelings, and attitudes, is often part of this content. Particularly in the context of an emergency, this information should also be used to facilitate decision making. The current blog recommendation model examines primarily users’ interests or content similarity, whereas in this paper, the value of the blog is considered. The primary contribution of this paper is the proposal of an information-entropy-based blog recommendation model for finding valuable blogs to facilitate decision-making in an emergency context. A series of indicators for evaluating a blog in an emergency context are proposed. Using the method of information entropy, a blog recommendation model is developed. The model can also be used to forecast the value of emergency blogs in the future. The model has been tested and validated using crawled data from the Sina Blog, and the results have demonstrated that the proposed model can effectively determine the value of emergency-related blogs.

scholarly journals A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Guangxia Xu ◽  
Zhijing Tang ◽  
Chuang Ma ◽  
Yanbing Liu ◽  
Mahmoud Daneshmand
Keyword(s):  
Collaborative Filtering ◽  
Traditional Method ◽  
Recommendation System ◽  
Rapid Development ◽  
Poor Performance ◽  
Mobile Internet ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
Similarity Calculation

Complex and diverse information is flooding entire networks because of the rapid development of mobile Internet and information technology. Under this condition, it is difficult for a person to locate and access useful information for making decisions. Therefore, the personalized recommendation system which utilizes the user’s behaviour information to recommend interesting items emerged. Currently, collaborative filtering has been successfully utilized in personalized recommendation systems. However, under the condition of extremely sparse rating data, the traditional method of similarity between users is relatively simple. Moreover, it does not consider that the user’s interest will change over time, which results in poor performance. In this paper, a new similarity measure method which considers user confidence and time context is proposed to preferably improve the similarity calculation between users. Finally, the experimental results demonstrate that the proposed algorithm is suitable for the sparse data and effectively improves the prediction accuracy and enhances the recommendation quality at the same time.

scholarly journals Game Theory-Based Smart Mobile-Data Offloading Scheme in 5G Cellular Networks

2020 ◽  
Vol 10 (7) ◽  
pp. 2327
Author(s):  
Huynh Thanh Thien ◽  
Van-Hiep Vu ◽  
Insoo Koo
Keyword(s):  
Traffic Congestion ◽  
Cellular Network ◽  
Rapid Development ◽  
Optimal Solution ◽  
Nash Bargaining Solution ◽  
Mobile Internet ◽  
Nash Bargaining ◽  
Smart Devices ◽  
Bargaining Solution ◽  
Mobile Data

Mobile-data traffic exponentially increases day by day due to the rapid development of smart devices and mobile internet services. Thus, the cellular network suffers from various problems, like traffic congestion and load imbalance, which might decrease end-user quality of service. This work compensates for the problem of offloading in the cellular network by forming device-to-device (D2D) links. A game scenario is formulated where D2D-link pairs compete for network resources. In a D2D-link pair, the data of a user equipment (UE) is offloaded to another UE with an offload coefficient, i.e., the proportion of requested data that can be delivered via D2D links. Each link acts as a player in a cooperative game, with the optimal solution for the game found using the Nash bargaining solution (NBS). The proposed solution aims to present a strategy to control different parameters of the UE, including harvested energy which is stored in a rechargeable battery with a finite capacity and the offload coefficients of the D2D-link pairs, to optimize the performance of the network in terms of throughput and energy efficiency (EE) while considering fairness among links in the network. Simulation results show that the proposed game scheme can effectively offload mobile data, achieve better EE and improve the throughput while maintaining high fairness, compared to an offloading scheme based on a maximized fairness index (MFI) and to a no-offload scheme.

VSumVis: Interactive Visual Understanding and Diagnosis of Video Summarization Model

2021 ◽  
Vol 12 (4) ◽  
pp. 1-28
Author(s):  
Guodao Sun ◽  
Hao Wu ◽  
Lin Zhu ◽  
Chaoqing Xu ◽  
Haoran Liang ◽  
...  
Keyword(s):  
User Study ◽  
Visual Analysis ◽  
Rapid Development ◽  
Video Summarization ◽  
Mobile Internet ◽  
User Centered Design ◽  
User Interactions ◽  
Multi Level ◽  
The Difference ◽  
Analysis System

With the rapid development of mobile Internet, the popularity of video capture devices has brought a surge in multimedia video resources. Utilizing machine learning methods combined with well-designed features, we could automatically obtain video summarization to relax video resource consumption and retrieval issues. However, there always exists a gap between the summarization obtained by the model and the ones annotated by users. How to help users understand the difference, provide insights in improving the model, and enhance the trust in the model remains challenging in the current study. To address these challenges, we propose VSumVis under a user-centered design methodology, a visual analysis system with multi-feature examination and multi-level exploration, which could help users explore and analyze video content, as well as the intrinsic relationship that existed in our video summarization model. The system contains multiple coordinated views, i.e., video view, projection view, detail view, and sequential frames view. A multi-level analysis process to integrate video events and frames are presented with clusters and nodes visualization in our system. Temporal patterns concerning the difference between the manual annotation score and the saliency score produced by our model are further investigated and distinguished with sequential frames view. Moreover, we propose a set of rich user interactions that enable an in-depth, multi-faceted analysis of the features in our video summarization model. We conduct case studies and interviews with domain experts to provide anecdotal evidence about the effectiveness of our approach. Quantitative feedback from a user study confirms the usefulness of our visual system for exploring the video summarization model.

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

scholarly journals Mapping the Sharing Economy in China

2020 ◽  
Vol 12 (16) ◽  
pp. 6333
Author(s):  
Chan Liu ◽  
Raymond K. H. Chan ◽  
Maofu Wang ◽  
Zhe Yang
Keyword(s):  
Global Economy ◽  
Rapid Development ◽  
Sharing Economy ◽  
Essential Elements ◽  
Mobile Internet ◽  
Internet Technology ◽  
Short Term ◽  
Bona Fide ◽  
The Government ◽  
Idle Capacity

Harnessing the rapid development of mobile internet technology, the sharing economy has experienced unprecedented growth in the global economy, especially in China. Likely due to its increasing popularity, more and more businesses have adopted this label in China. There is a concern as to the essential meaning of the sharing economy. As it is difficult to have a universally accepted definition, we aim to map the sharing economy and demystify the use of it in China in this paper. We propose seven organizing essential elements of the sharing economy: access use rights instead of ownership, idle capacity, short term, peer-to-peer, Internet platforms mediated, for monetary profit, and shared value orientation. By satisfying all or only parts of these elements, we propose one typology of sharing economy, and to differentiate bona fide sharing economy from quasi- and pseudo-sharing economy. Finally, there are still many problems that need to be solved urgently in the real sharing economy from the perspective of the government, companies and individuals.

Dynamic network intelligent hybrid recommendation algorithm and its application in online shopping platform

2021 ◽  
pp. 1-13
Author(s):  
Yuxuan Gao ◽  
Haiming Liang ◽  
Bingzhen Sun
Keyword(s):  
Online Shopping ◽  
Rapid Development ◽  
Dynamic Network ◽  
Topsis Method ◽  
Recommendation Algorithm ◽  
Time Periods ◽  
The Difference ◽  
Relationship Of ◽  
Hybrid Recommendation ◽  
Novel Model

With the rapid development of e-commerce, whether network intelligent recommendation can attract customers has become a measure of customer retention on online shopping platforms. In the literature about network intelligent recommendation, there are few studies that consider the difference preference of customers in different time periods. This paper proposes the dynamic network intelligent hybrid recommendation algorithm distinguishing time periods (DIHR), it is a integrated novel model combined with the DEMATEL and TOPSIS method to solved the problem of network intelligent recommendation considering time periods. The proposed method makes use of the DEMATEL method for evaluating the preference relationship of customers for indexes of merchandises, and adopt the TOPSIS method combined with intuitionistic fuzzy number (IFN) for assessing and ranking the merchandises according to the indexes. We specifically introduce the calculation steps of the proposed method, and then calculate its application in the online shopping platform.

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