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An efficient hybrid recommendation model based on collaborative filtering recommender systems

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CAAI Transactions on Intelligence Technology 2021年第4期6卷 480-492页

作者： Mohammed Fadhel Aljunid Manjaiah Doddaghatta Huchaiah Department of Computer Science Mangalore UniversityMangaloreIndia

In recent years,collaborative filtering(CF)techniques have become one of the most popularly used techniques for providing personalised services to users.CF techniques collect users9 previous information about items such as books,music,movies,ideas,and so on.Memory-based models are generally referred to as similarity-based CF models,which are one of the most widely agreeable approaches for providing service recommendations.The memory-based approach includes user-based CF(UCF)and item-based CF(ICF)algorithms.The UCF model recommends items by finding similar users,while the ICF model recommends items by finding similar items based on the user-item rating matrix.However,consequent to the ingrained sparsity of the user-item rating matrix,a large number of ratings are missing.This results in the availability of only a few ratings to make predictions for the unknown ratings.The result is the poor prediction quality of the CF model.A model to find the best algorithm is provided here,which gives the most accurate recommendation based on different similarity metrics.Here a hybrid recommendation model,namely rUICF,is proposed.The rUICF model integrates the UCF and ICF models with the T linear regression model to model the sparsity and scalability issue of the user-item rating matrix.Detailed experimentation on two different real-world datasets shows that the proposed model demonstrates substantial performance when compared with the existing methods.

关键词： services filtering similarity

Multi-model deep learning approach for collaborative filtering recommendation system

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CAAI Transactions on Intelligence Technology 2020年第4期5卷 268-275页

作者： Mohammed Fadhel Aljunid Manjaiah Doddaghatta Huchaiah Computer Science Mangalore UniversityMangaloreIndia

As a result of a huge volume of implicit feedback such as browsing and clicks,many researchers are involving in designing recommender systems(RSs)based on implicit feedback.Though implicit feedback is too challenging,it is highly applicable to use in building recommendation systems.Conventional collaborative filtering techniques such as matrix decomposition,which consider user preferences as a linear combination of user and item latent features,have limited learning capacities,hence suffer from a cold start and data sparsity problems.To tackle these problems,the research direction towards considering the integration of conventional collaborative filtering with deep neural networks to maps user and item features.Conversely,the scalability and the sparsity of the data affect the performance of the methods and limit the worthiness of the results of the recommendations.Therefore,the authors proposed a multimodel deep learning(MMDL)approach by integrating user and item functions to construct a hybrid RS and significant improvement.The MMDL approach combines deep autoencoder with a one-dimensional convolution neural network model that learns user and item features to predict user preferences.From detail experimentation on two real-world datasets,the proposed work exhibits substantial performance when compared to the existing methods.

关键词： filtering approach neural

Distributed filtering and control of complex networks and systems

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Frontiers of Information Technology & Electronic Engineering 2021年第1期22卷 1-4页

作者： Guanrong CHEN Sergej CELIKOVSKY Lei GUO Youmin ZHANG Tiancheng LI Depariment of Electrical Engineering City University of Hong KongHong KongChina The Czech Academy of Sciences Institute of Imnformation Theory and AutomationPrague 18200Czech Republic School of Automation Science and Electrical Engineering Beihang UniversityBejjing 100191China Department of Mechanical Industrial and Aerospace EngineeringConcordia UniversitMontrealQuebec H3G 1M8Canada MOE Key Laboratory of Information Pusion Technology School ofAutomationNorthwestern Polytechnical UniversiyXi'an 710072China

In the last two decades,research on distributed filtering and control(FAC)has burgeoned into large-scale complex networks and systems.A distributed networked system,typically composed of a number of cost-effective dynamical nodes(agents)with limited sensing,computing,and communication capabilities,is gaining considerable popularity due to its low power consumption,simple installation,high performance,and strong reliability,as compared with the centralized setting.As a result,various networkinduced FAC systems,techniques,and algorithms have emerged,including node registration and control,network consensus and synchronization,multi-sensor data clustering/fusion,network topological design and analysis,and the like.

关键词： networks filtering network

Modified Cortex Mori Capsules improving the successful rate of functional filtering blebs after reclinical glaucoma filtering surgery

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World Journal of Clinical Cases 2019年第21期7卷 3436-3445页

作者： Jing Yu Li-Xin Qiu Guo-Ping Qing Bo-Wen Zhao Hui Wang Beijing Tongren Eye Center Laboratory of Ophthalmology and Visual Science in Beijing Beijing Tongren HospitalCapital Medical University

BACKGROUND The major reason for filtering bleb failure or scarring of the bleb site is due to excessive scarring after glaucoma filtration surgery in the clinic.Traditional Chinese medicine has preeminence in the prevention of fibrosis formation through the regulation of systemic circulation and improvement of the properties of the inflammatory cells in the blood.AIM To examine the clinical efficacy of using the Modified Cortex Mori Capsules(MCMC;Chinese name:Jiawei Sangbaipi Capsules)in the success rate of functional filtering blebs after glaucoma filtering surgery in clinical patients.METHODS Sixty resurgery glaucoma patients were randomly divided into two groups:30 patients in surgery with the placebo group and 30 patients in surgery with the MCMC group.Patients took either the placebo or the MCMC 2 wk before and after surgery.Postoperative morphology and function filtering bleb,visual acuity,intraocular pressure,postoperative complications,the success rate of filtration surgery and clinical efficacy were observed.RESULTS Fifty patients completed the study.The percentage of functional filtering blebs in the surgery plus MCMC group was 84%at 6 mo after surgery,which was higher than surgery plus placebo group(64%,Pfiltering surgery while taking MCMC not only reduced excessive scar formation and increased the success rate of functional filtering blebs but also improved the success of glaucoma filtration operations.

关键词： Glaucoma Traditional Chinese medicine Modified Cortex Mori Capsules Jiawei Sangbaipi Capsules filtering bleb Scar formation

Sensitivity analysis of parameters and contrasting performance of ground filtering algorithms with UAV photogrammetry-based and LiDAR point clouds

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International Journal of Digital Earth 2020年第12期13卷 1672-1694页

作者： Petr Klápště Michal Fogl Vojtěch Barták Kateřina Gdulová Rudolf Urban Vítězslav Moudrý Department of Applied Geoinformatics and Spatial Planning Faculty of Environmental SciencesCzech University of Life Sciences PragueKamýcká129Praha-Suchdol16500Czech Republic Department of Special Geodesy Faculty of Civil EngineeringCzech Technical University in PraguePragueCzech Republic

Most ground filtering algorithms are primarily designed for airborne LiDAR point cloud processing and their successful use in identifying ground points from photogrammetric point clouds remains questionable.We compared six ground filtering algorithms implemented in Metashape,ArcGIS,CloudCompare,LAStools,and PDAL.We used UAV photogrammetry-based(acquired under leaf-off conditions)and airborne LiDAR(leaf-on)point clouds of the same area to:(i)compare accuracy of generated DTMs;(ii)evaluate the effect of vegetation density and terrain slope on filtering accuracy;and(iii)assess which algorithm parameters have the greatest effect on the filtering accuracy.Our results show that the performance of filtering algorithms was affected by the point cloud type,terrain slope and vegetation cover.The results were generally better for LiDAR(RMSE 0.13–0.19 m)than for photogrammetric(RMSE 0.19–0.23 m)point clouds.The behavior in varying vegetation and terrain conditions was consistent for LiDAR point clouds.However,when applied on photogrammetric point clouds,the algorithms’behavior was inconsistent,especially in areas of steep slope(except for the Progressive Triangulated Irregular Network in LAStools).Parameters related to the selection of the initial minimum elevation ground points were the most influential in all algorithms and point clouds.

关键词： DTM filtering LiDAR point cloud spoil heap UAV

Monitoring Multiple Cropping Index of Henan Province, China Based on MODIS-EVI Time Series Data and Savitzky-Golay filtering Algorithm

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Computer Modeling in Engineering & Sciences 2019年第5期119卷 331-348页

作者： Lihui Wang Feng Qi Xin Shen Jinliang Huang Key Laboratory of Environment and Disaster Monitoring and Evaluation Institute of Geodesy and GeophysicsChinese Academy of SciencesWuhan430077China State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhan430079China Collaborative Innovation Center of Geospatial Technology Wuhan UniversityWuhan430079China.

Multiple cropping index(MCI)is a very important indicator in crop production and agricultural intensification,which represents the utilizing degree of agriculture resources at time scale and the effective utilization situation of arable land.The objective of this paper is monitoring multiple cropping index of Henan province of China according to the time series of MODIS(Moderate-Resolution Imaging Spectroradiometer)EVI(Enhanced Vegetation Index)after Savitzky-Golay filter processing from the year 2006 to 2011.The results revealed that this method could provide an effective way to monitor multiple cropping index,and the method of no additional authentication data is independent and reliable.The result was accurate and stable,the slope of linear regression of the multiple cropping index between the statistical results and the remote sensing results was 1.0136(R2=0.779).The precision of sample areas validation was 97.91%.Suggesting that the time series MODIS-EVI which after Savitzky-Golay filtering processed,could provide an effective way to extract spatial information of multiple cropping index for management department of agriculture.

关键词： Multiple cropping index time Series of MODIS-EVI Savitzky-Golay filtering Henan Province

Frequency Domain filtering SAR Interferometric Phase Noise Using the Amended Matrix Pencil Model

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Computer Modeling in Engineering & Sciences 2019年第5期119卷 349-363页

作者： Yandong Gao Shubi Zhang Kefei Zhang Shijin Li School of Environment Science and Spatial Informatics China University of Miningand TechnologyXuzhou221116China SPACE Research Centre School of Mathematical and Geospatial SciencesRMIT UniversityMelbourneVictoria3000Australia

Interferometric phase filtering is one of the key steps in interferometricsynthetic aperture radar (InSAR/SAR). However, the ideal filtering results are difficult toobtain due to dense fringe and low coherence regions. Moreover, the InSAR/SAR datarange is relatively large, so the efficiency of interferential phase filtering is one of themajor problems. In this letter, we proposed an interferometric phase filtering methodbased on an amended matrix pencil and linear window mean filter. The combination ofthe matrix pencil and the linear mean filter are introduced to the interferometric phasefiltering for the first time. First, the interferometric signal is analyzed, and theinterferometric phase filtering is transformed into a local frequency estimation problem.Then, the local frequency is estimated using an amended matrix pencil at a window. Thelocal frequency can represent terrain changes, thus suggesting that the frequency can beaccurately estimated even in dense fringe regions. Finally, the local frequency is filteredby using a linear window mean filter, and the filtered phase is recovered. The proposedmethod is calculated by some matrices. Therefore, the computational complexity isreduced, and the efficiency of the interferometric phase filtering is improved.Experiments are conducted with simulated and real InSAR data. The proposed methodexhibits a better filtering effect and an ideal efficiency as compared with the traditionalfiltering method.

关键词： Interferometric phase filtering interferometric synthetic aperture radar local frequency estimation amended matrix pencil linear window mean filter

A Novel Deep Hybrid Recommender System Based on Auto-encoder with Neural Collaborative filtering

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Big Data Mining and Analytics 2018年第3期1卷 211-221页

作者： Yu Liu Shuai Wang M.Shahrukh Khan Jieyu He School of Computer Science and Engineering and also with MOE Key Laboratory of Computer Network and Information Integration Southeast University

Due to the widespread availability of implicit feedback(e.g., clicks and purchases), some researchers have endeavored to design recommender systems based on implicit feedback. However, unlike explicit feedback,implicit feedback cannot directly reflect user preferences. Therefore, although more challenging, it is also more practical to use implicit feedback for recommender systems. Traditional collaborative filtering methods such as matrix factorization, which regards user preferences as a linear combination of user and item latent vectors, have limited learning capacities and suffer from data sparsity and the cold-start problem. To tackle these problems,some authors have considered the integration of a deep neural network to learn user and item features with traditional collaborative filtering. However, there is as yet no research combining collaborative filtering and contentbased recommendation with deep learning. In this paper, we propose a novel deep hybrid recommender system framework based on auto-encoders(DHA-RS) by integrating user and item side information to construct a hybrid recommender system and enhance performance. DHA-RS combines stacked denoising auto-encoders with neural collaborative filtering, which corresponds to the process of learning user and item features from auxiliary information to predict user preferences. Experiments performed on the real-world dataset reveal that DHA-RS performs better than state-of-the-art methods.

关键词： hybrid recommender system neural collaborative filtering auto-encoder implicit feedback

Enhanced Userspace and In-Kernel Trace filtering for Production Systems

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Journal of Computer Science & Technology 2016年第6期31卷 1161-1178页

作者： Suchakrapani Datt Sharma Michel Dagenais Department of Computer and Software Engineering Ecole Polytechnique de Montrdal Montreal H3T 1J4 Canada

Trace tools like LTTng have a very low impact on the traced software as compared with traditional debuggers. However, for long runs, in resource constrained and high throughput environments, such as embedded network switching nodes and production servers, the collective tracing impact on the target software adds up considerably. The overhead is not just in terms of execution time but also in terms of the huge amount of data to be stored, processed and analyzed offiine. This paper presents a novel way of dealing with such huge trace data generation by introducing a Just-In-Time （JIT） filter based tracing system, for sieving through the flood of high frequency events, and recording only those that are relevant, when a specific condition is met. With a tiny filtering cost, the user can filter out most events and focus only on the events of interest. We show that in certain scenarios, the JIT compiled filters prove to be three times more effective than similar interpreted filters. We also show that with the increasing number of filter predicates and context variables, the benefits of JIT compilation increase with some JIT compiled filters being even three times faster than their interpreted counterparts. We further present a new architecture, using our filtering system, which can enable co-operative tracing between kernel and process tracing VMs （virtual machines） that share data efficiently. We demonstrate its use through a tracing scenario where the user can dynamically specify syscall latency through the userspace tracing VM whose effect is reflected in tracing decisions made by the kernel tracing VM. We compare the data access performance on our shared memory system and show an almost 100 times improvement over traditional data sharing for co-operative tracing.

关键词： tracing debugging filtering operating system interpreter