A New Time-Aware Collaborative Filtering Intelligent Recommendation System

Jiang, Weijin; Chen, Jiahui; Jiang, Yi-Rong; Xu, Yuhui; Wan, Shaohua; Tan, L. C.; Liang, Guang Chuan

doi:10.32604/cmc.2019.05932

Cited by 27 publications

(18 citation statements)

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“…However, it has the disadvantage of too coarse recommendation granularity, and the collected information may be false, which will affect the prediction results. e content-based recommendation system obtains the user's interest preference by analyzing the user's use or viewing history and then compares the similarity between the user's interest description and the resource content and sorts the resources to recommend [10].…”

Section: Common Methods Of the Intelligent Recommendationmentioning

confidence: 99%

Intelligent Recommendation System Based on Mathematical Modeling in Personalized Data Mining

Cui

2021

Mathematical Problems in Engineering

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With the advent of the era of big data, data mining has become one of the key technologies in the field of research and business. In order to improve the efficiency of data mining, this paper studies data mining based on the intelligent recommendation system. Firstly, this paper makes mathematical modeling of the intelligent recommendation system based on association rules. After analyzing the requirements of the intelligent recommendation system, Java 2 Platform, Enterprise Edition, technology is used to divide the system architecture into the presentation layer, business logic layer, and data layer. Recommendation module is divided into three substages: data representation, model learning, and recommendation engine. Then, the fuzzy clustering algorithm is used to optimize the system. After the system is built, the performance of the system is evaluated, and the evaluation indexes include accuracy, coverage, and response time. Finally, the system is put into a trial operation of an e-commerce platform. The click-through rate and purchase conversion rate of recommended products before and after the operation are compared, and a questionnaire survey is randomly launched to the platform users to analyze the user satisfaction. The experimental data show that the MAE of this system is the lowest, maintained at about 0.73, and its accuracy is the highest; before the recommended threshold exceeds 0.5, the average coverage rate of this system is the highest: 0.75; in Q1–Q5 subsets, the shortest response time of the system is 0.2 s. Before and after the operation of the system, the average click-through rate increased by 11.04%, and the average purchase rate increased by 9.35%. Among the 1216 users, 43% of the users were satisfied with 4 and 9% with 1. This shows that the system algorithm convergence speed is fast; it can recommend products more in line with user needs and interests and promote higher click-through rate and purchase rate, but user satisfaction can be further improved.

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Section: Common Methods Of the Intelligent Recommendationmentioning

confidence: 99%

Intelligent Recommendation System Based on Mathematical Modeling in Personalized Data Mining

Cui

2021

Mathematical Problems in Engineering

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“…The result obtained by using the regression model and the prediction method is very small, and there is a decline in some indicators. After studying the literature related to this article, add relevant theoretical and experimental analysis [20][21][22][23][24] .Comprehensive consideration, the use of MBPR performs better.…”

Section: Network Element Optimization Experiments Analysismentioning

confidence: 99%

A Method of Optimizing Network Topology Structure Combining Viterbi Algorithm and Bayesian Algorithm

Shi

2021

Wireless Communications and Mobile Computing

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With Internet entering all walks of life, development of internet and usage expansion demand better performance, especially the application of 5G network that adopts NAS networking mode. Some of the network bandwidth cannot fully support the current network demand, which causes network fluctuations and other concerns. In this paper, a method for optimizing the topological structure of the bottom layer of the communication network is proposed that has outage performance close to optimal routing scheme. In specific, path in areas with poor network conditions is first optimized using Viterbi algorithm. Then, network element nodes on the path are optimized using Bayes recommendation algorithm for reasonable flow distribution. Dual planning of improved Viterbi algorithm is used to realize the main and standby path planning, and then, Bayesian recommendation algorithm based on the average value is used to optimize the network elements. Therefore, it is very efficient to realize overall performance optimization.

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“…Then the matching score function is used to determine the similarity between the current object candidate state and the object template of the first frame during the tracking, which improves the tracking efficiency. In recent years, the generative adversarial network (GAN) has been widely used in many fields, such as object detection [36] and intelligent recommendation system [37]. GAN is first proposed by Ian Goodfellow in 2014, which was originally used to generate realistic-looking images [38].…”

Section: Related Workmentioning

confidence: 99%

Learning Attention for Object Tracking with Adversarial Learning Network

Cheng

Song

et al. 2020

Preprint

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Artificial intelligence has been widely studied on solving intelligent surveillance analysis and security problems in recent years. Although many multimedia security approaches have been proposed by using deep learning network model, there are still some challenges on their performances which deserve in-depth research. On one hand, high computational complexity of current deep learning methods makes it hard to be applied to real-time scenario. On the other hand, it is difficult to obtain the specific features of a video by fine-tuning the network online with the object state of the first frame, which fails to capture rich appearance variations of the object. To solve above two issues, in this paper, an effective object tracking method with learning attention is proposed to achieve the object localization and reduce the training time in adversarial learning framework. First, a prediction network is designed to track the object in video sequences. The object positions of the first ten frames are employed to fine-tune prediction network, which can fully mine a specific features of an object.Second, the prediction network is integrated into the generative adversarial network framework, which randomly generates masks to capture object appearance variations via adaptively dropout input features. Third, we present a spatial attention mechanism to improve the tracking performance.The proposed network can identify the mask that maintains the most robust features of the objects over a long temporal span. Extensive experiments on two large-scale benchmarks demonstrate that the proposed algorithm performs favorably against state-of-the-art methods. IntroductionWith rapid growth and use of multimedia signal processing and Internet technology, a number of multimedia security issues have also emerged correspondingly in recent years, such as intelligent analysis for surveillance, copy-move forgery in digital images and videos, and biometric spoofing.Meanwhile, artificial intelligence has been widely studied on solving a variety of difficult problems using deep learning network model, such as convolution neural networks for steganalysis and forensics, and generative adversarial networks for coverless steganagraphy.Intelligent analysis for surveillance technology has been a hot topic in multimedia security community. It is the basis of advanced video processing tasks such as follow-up steganography [1], SRDCF with multiple training samples, but also provide a more robust appearance model than SRDCF in the case of large appearance variations. Although these approaches achieve a satisfied performance in some constrained scenarios, they have an inherent limitation that they resort to low level hand-crafted features, which are vulnerable in dynamic situations including illumination changes, occlusion, deformations, background clutter etc. Inspired by the success of deep learning model in object detection, recognition and classification tasks, researchers have started to focus on combining of deep learning and correlation filter. In HC...

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A New Time-Aware Collaborative Filtering Intelligent Recommendation System

Cited by 27 publications

References 0 publications

Intelligent Recommendation System Based on Mathematical Modeling in Personalized Data Mining

Intelligent Recommendation System Based on Mathematical Modeling in Personalized Data Mining

A Method of Optimizing Network Topology Structure Combining Viterbi Algorithm and Bayesian Algorithm

Learning Attention for Object Tracking with Adversarial Learning Network

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