Improving Recommendation Accuracy and Diversity via Multiple Social Factors and Social Circles

2019 ◽  
pp. 132-154
Author(s):  
Yong Feng ◽  
Heng Li ◽  
Zhuo Chen ◽  
Baohua Qiang
Keyword(s):  
Social Factors ◽  
Online Social Networks ◽  
Online Information ◽  
Information Discovery ◽  
Proposed Model ◽  
Probabilistic Matrix Factorization ◽  
Series Of Experiments ◽  
Networks Analysis ◽  
Recommendation Accuracy ◽  
Improved Performance

Recommender systems have been widely employed to suggest personalized online information to simplify users' information discovery process. With the popularity of online social networks, analysis and mining of social factors and social circles have been utilized to support more effective recommendations, but have not been fully investigated. In this chapter, the authors propose a novel recommendation model with the consideration of more comprehensive social factors and topics. To further enhance recommendation accuracy, four social factors are simultaneously injected into the recommendation model based on probabilistic matrix factorization. Meanwhile, the authors explore several new methods to measure these social factors. Moreover, they infer explicit and implicit social circles to enhance the performance of recommendation diversity. Finally, the authors conduct a series of experiments on publicly available data. Experimental results show the proposed model achieves significantly improved performance over the existing models in which social information have not been fully considered.

Improving Recommendation Accuracy and Diversity via Multiple Social Factors and Social Circles

2014 ◽  
Vol 11 (4) ◽  
pp. 32-46 ◽  
Author(s):  
Yong Feng ◽  
Heng Li ◽  
Zhuo Chen
Keyword(s):  
Social Factors ◽  
Online Social Networks ◽  
Individual Preference ◽  
Online Information ◽  
Information Discovery ◽  
Interpersonal Closeness ◽  
Probabilistic Matrix Factorization ◽  
Recommendation Accuracy ◽  
Improved Performance ◽  
Interest Similarity

Recommender systems (RS) have been widely employed to suggest personalized online information to simplify user's information discovery process. With the popularity of online social networks, analysis and mining of social factors and social circles have been utilized to support more effective recommendations, but have not been fully investigated. In this paper, the authors propose a novel recommendation model with the consideration of more comprehensive social factors and topics that user is explicitly and implicitly interested in. Concretely, to further enhance recommendation accuracy, four social factors, individual preference, interpersonal trust influence, interpersonal interest similarity and interpersonal closeness degree, are simultaneously injected into our recommendation model based on probabilistic matrix factorization. Meanwhile, the authors explore several new methods to measure these social factors. Moreover, the authors infer explicit and implicit social circles to enhance the performance of recommendation diversity. Finally, the authors conduct a series of experiments on publicly available data. Experimental results show the proposed model achieves significantly improved performance (accuracy and diversity) over the existing models in which social information have not been fully considered.

scholarly journals Machine Learning and Prediction-Based Resource Management in IoT Considering Qos

2019 ◽  
Vol 8 (2) ◽  
pp. 687-694
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Short Term Memory ◽  
Real Data ◽  
Computing Methods ◽  
Energy Utilization ◽  
Real Field ◽  
Proposed Model ◽  
Improved Performance ◽  
Video Sensors

Internet of Things (IoT) is one of the fast-growing technology paradigms used in every sectors, where in the Quality of Service (QoS) is a critical component in such systems and usage perspective with respect to ProSumers (producer and consumers). Most of the recent research works on QoS in IoT have used Machine Learning (ML) techniques as one of the computing methods for improved performance and solutions. The adoption of Machine Learning and its methodologies have become a common trend and need in every technologies and domain areas, such as open source frameworks, task specific algorithms and using AI and ML techniques. In this work we propose an ML based prediction model for resource optimization in the IoT environment for QoS provisioning. The proposed methodology is implemented by using a multi-layer neural network (MNN) for Long Short Term Memory (LSTM) learning in layered IoT environment. Here the model considers the resources like bandwidth and energy as QoS parameters and provides the required QoS by efficient utilization of the resources in the IoT environment. The performance of the proposed model is evaluated in a real field implementation by considering a civil construction project, where in the real data is collected by using video sensors and mobile devices as edge nodes. Performance of the prediction model is observed that there is an improved bandwidth and energy utilization in turn providing the required QoS in the IoT environment.

scholarly journals Propagation Model of Panic Buying Under the Sudden Epidemic

2021 ◽  
Vol 9 ◽  
Author(s):  
Peihua Fu ◽  
Bailu Jing ◽  
Tinggui Chen ◽  
Chonghuan Xu ◽  
Jianjun Yang ◽  
...  
Keyword(s):  
Online Social Networks ◽  
Group Behavior ◽  
Propagation Model ◽  
Release Time ◽  
External Information ◽  
Buying Behavior ◽  
Factors Affecting ◽  
Proposed Model ◽  
Huge Impact ◽  
Number Of Individuals

The sudden outbreak of COVID-19 at the end of 2019 has had a huge impact on people's lives all over the world, and the overwhelmingly negative information about the epidemic has made people panic for the future. This kind of panic spreads and develops through online social networks, and further spreads to the offline environment, which triggers panic buying behavior and has a serious impact on social stability. In order to quantitatively study this behavior, a two-layer propagation model of panic buying behavior under the sudden epidemic is constructed. The model first analyzes the formation process of individual panic from a micro perspective, and then combines the Susceptible-Infected-Recovered (SIR) Model to simulate the spread of group behavior. Then, through simulation experiments, the main factors affecting the spread of panic buying behavior are discussed. The experimental results show that: (1) the dissipating speed of individual panics is related to the number of interactions and there is a threshold. When the number of individuals involved in interacting is equal to this threshold, the panic of the group dissipates the fastest, while the dissipation speed is slower when it is far from the threshold; (2) The reasonable external information release time will affect the occurrence of the second panic buying, meaning providing information about the availability of supplies when an escalation of epidemic is announced will help prevent a second panic buying. In addition, when the first panic buying is about to end, if the scale of the second panic buying is to be suppressed, it is better to release positive information after the end of the first panic buying, rather than ahead of the end; and (3) Higher conformity among people escalates panic, resulting in panic buying. Finally, two cases are used to verify the effectiveness and feasibility of the proposed model.

scholarly journals Augmented Context-Based Conceptual User Modeling for Personalized Recommendation System in Online Social Networks

2020 ◽  
Vol 14 (3) ◽  
pp. 1-19
Author(s):  
Ammar Alnahhas ◽  
Bassel Alkhatib
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Recommendation System ◽  
Contextual Information ◽  
Detailed Comparison ◽  
Personalized Recommendation ◽  
User Models ◽  
Proposed Model ◽  
Recommendation Algorithms ◽  
Novel Method

As the data on the online social networks is getting larger, it is important to build personalized recommendation systems that recommend suitable content to users, there has been much research in this field that uses conceptual representations of text to match user models with best content. This article presents a novel method to build a user model that depends on conceptual representation of text by using ConceptNet concepts that exceed the named entities to include the common-sense meaning of words and phrases. The model includes the contextual information of concepts as well, the authors also show a novel method to exploit the semantic relations of the knowledge base to extend user models, the experiment shows that the proposed model and associated recommendation algorithms outperform all previous methods as a detailed comparison shows in this article.

scholarly journals A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Lu Yu ◽  
Jianling Qu ◽  
Feng Gao ◽  
Yanping Tian
Keyword(s):  
Fault Diagnosis ◽  
Short Term Memory ◽  
Operating Conditions ◽  
Support Vector ◽  
Rolling Bearings ◽  
Hierarchical Algorithm ◽  
Bearing Fault Diagnosis ◽  
Rotary Machinery ◽  
Proposed Model ◽  
Series Of Experiments

Faced with severe operating conditions, rolling bearings tend to be one of the most vulnerable components in mechanical systems. Due to the requirements of economic efficiency and reliability, effective fault diagnosis methods for rolling bearings have long been a hot research topic of rotary machinery fields. However, traditional methods such as support vector machine (SVM) and backpropagation neural network (BP-NN) which are composed of shallow structures trap into a dilemma when further improving their accuracies. Aiming to overcome shortcomings of shallow structures, a novel hierarchical algorithm based on stacked LSTM (long short-term memory) is proposed in this text. Without any preprocessing operation or manual feature extraction, the proposed method constructs a framework of end-to-end fault diagnosis system for rolling bearings. Beneficial from the memorize-forget mechanism of LSTM, features inherent in raw temporal signals are extracted hierarchically and automatically by stacking LSTM. A series of experiments demonstrate that the proposed model can not only achieve up to 99% accuracy but also outperform some state-of-the-art intelligent fault diagnosis methods.

Evolutionary Intelligence-Based Feature Descriptor Selection for Efficient Identification of Anti-Cancer Peptides

2020 ◽  
pp. 176-191
Author(s):  
Deepak Singh ◽  
Dilip Singh Sisodia ◽  
Pradeep Singh
Keyword(s):  
Feature Selection ◽  
Amino Acid ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Model Building ◽  
Predictive Performance ◽  
Feature Descriptors ◽  
Transition Distribution ◽  
Proposed Model ◽  
Improved Performance

A novel evolutionary-based feature selection model for ACPs identification that will explore the relationships hidden across the various feature descriptors is explored in this chapter. In this model, the authors amalgamate the nine feature descriptors from the three groups of peptide feature descriptors including amino acid composition (three descriptors), grouped amino acid composition and composition/transition/distribution (three descriptors). The proposed model integrates these features to unfold the hidden association between the diverse features in peptide classification. However, the inclusion of irrelevant, redundant, and noisy attributes in the model building process phase can result in poor predictive performance and increased computation. Hence, evolutionary-based feature selection is utilized in the model that involves a combination of search and feature utility estimation by ReliefF score. Through extensive experiments on benchmark dataset, it is demonstrated that the proposed model achieves improved performance.

Damage-coupled ratcheting behaviors of SA508 Gr.3 steel at room and elevated temperatures: Experiments and simulations

2020 ◽  
Vol 29 (9) ◽  
pp. 1379-1396
Author(s):  
Jun Tian ◽  
Xiaolong Fu ◽  
Xuejiao Shao ◽  
Lu Jiang ◽  
Jian Li ◽  
...  
Keyword(s):  
Path Dependence ◽  
Elevated Temperatures ◽  
Kinematic Hardening ◽  
Cyclic Softening ◽  
Dynamic Strain ◽  
Loading Paths ◽  
Proposed Model ◽  
Series Of Experiments ◽  
Hardening Rules ◽  
Multiaxial Loadings

A series of experiments subjected to uniaxial and non-proportionally multiaxial cyclic loadings were performed to investigate the ratcheting responses of SA508 Gr.3 steel at room and elevated temperatures. The influences of different stress levels and nonproportional loading paths on the damage-coupled ratcheting responses were discussed. From experimental results, cyclic softening characteristic and dynamic strain aging can be observed under cyclic loadings. Moreover, the steel exhibits an obvious nonproportional path-dependence of the damage evolution under multiaxial loading paths. To numerically simulate the ratcheting responses under uniaxial and multiaxial loadings with the extended cyclic plastic model, the damage-coupled variable was introduced into the classic isotropic and nonlinear kinematic hardening rules. Corresponding material parameters could be calibrated from experimental data, and comparisons between experimental and simulated results were performed to validate the proposed model.

An Accurate Full Car Ride Model Using Model Reducing Techniques

2002 ◽  
Vol 124 (4) ◽  
pp. 697-705 ◽  
Author(s):  
Chul Kim ◽  
Paul I. Ro
Keyword(s):  
Model Complexity ◽  
Ride Quality ◽  
Singular Perturbation Method ◽  
Suspension Systems ◽  
Proposed Model ◽  
Model Size ◽  
Improved Performance ◽  
Accurate Matching ◽  
Multibody Dynamics Model ◽  
Selection Of

In this study, an approach to obtain an accurate yet simple model for full-vehicle ride analysis is proposed. The approach involves linearization of a full car MBD (multibody dynamics) model to obtain a large-order vehicle model. The states of the model are divided into two groups depending on their effects on the ride quality and handling performance. Singular perturbation method is then applied to reduce the model size. Comparing the responses of the proposed model and the original MBD model shows an accurate matching between the two systems. A set of identified parameters that makes the well-known seven degree-of-freedom model very close to the full car MBD model is obtained. Finally, the benefits of the approach are illustrated through design of an active suspension system. The identified model exhibits improved performance over the nominal models in the sense that the accurate model leads to the appropriate selection of control gains. This study also provides an analytical method to investigate the effects of model complexity on model accuracy for vehicle suspension systems.

An independent time optimized hybrid infrastructure for big data analytics

2020 ◽  
Vol 34 (28) ◽  
pp. 2050311
Author(s):  
Satvik Vats ◽  
B. B. Sagar
Keyword(s):  
Big Data ◽  
Execution Time ◽  
Resource Sharing ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Demilitarized Zone ◽  
Proposed Model ◽  
Hadoop Distributed File System ◽  
Improved Performance ◽  
Data Independence

In Big data domain, platform dependency can alter the behavior of the business. It is because of the different kinds (Structured, Semi-structured and Unstructured) and characteristics of the data. By the traditional infrastructure, different kinds of data cannot be processed simultaneously due to their platform dependency for a particular task. Therefore, the responsibility of selecting suitable tools lies with the user. The variety of data generated by different sources requires the selection of suitable tools without human intervention. Further, these tools also face the limitation of recourses to deal with a large volume of data. This limitation of resources affects the performance of the tools in terms of execution time. Therefore, in this work, we proposed a model in which different data analytics tools share a common infrastructure to provide data independence and resource sharing environment, i.e. the proposed model shares common (Hybrid) Hadoop Distributed File System (HDFS) between three Name-Node (Master Node), three Data-Node and one Client-node, which works under the DeMilitarized zone (DMZ). To realize this model, we have implemented Mahout, R-Hadoop and Splunk sharing a common HDFS. Further using our model, we run [Formula: see text]-means clustering, Naïve Bayes and recommender algorithms on three different datasets, movie rating, newsgroup, and Spam SMS dataset, representing structured, semi-structured and unstructured, respectively. Our model selected the appropriate tool, e.g. Mahout to run on the newsgroup dataset as other tools cannot run on this data. This shows that our model provides data independence. Further results of our proposed model are compared with the legacy (individual) model in terms of execution time and scalability. The improved performance of the proposed model establishes the hypothesis that our model overcomes the limitation of the resources of the legacy model.

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