Case Recommendation Based on Fuzzy Clustering in Personalization Web Services

2013 ◽  
Vol 791-793 ◽  
pp. 1760-1763
Author(s):  
Xue Gang Chen ◽  
Jia Lu Zhang ◽  
Jie Ren Cheng
Keyword(s):  
Real Time ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Recommendation System ◽  
Nearest Neighbors ◽  
Classification Model ◽  
Personalized Recommendation ◽  
Case Based Reasoning ◽  
Time Performance ◽  
Universe Of Discourse

Aiming at the problems of no strong real-time performance and poor scalability using traditional filtering recommendation technology, and a novel case recommended based on fuzzy clustering is proposed in this paper. Using case-based reasoning technology to personalized recommendation system, and the old case is clustered using fuzzy clustering algorithm, and a classification model is built, and the target case-the case base is converted to the target case-case class to reduce the most case retrieval space of the target cases the nearest neighbors. The experimental results indicate that this method can effectively improve the real-time performance, and it is used in E-commerce recommendation systems, and the degree of recommendation results universe of discourse is improved.

A Fuzzy Clustering-Based X-Ray Computed Tomography Simulation under Incomplete Measurements

2013 ◽  
Vol 748 ◽  
pp. 651-654
Author(s):  
Shi Hong Yue ◽  
Xiu Juan Bao ◽  
Jin Xin Zhang
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Real Time ◽  
Fuzzy Clustering ◽  
Time Complexity ◽  
Clustering Algorithm ◽  
X Ray ◽  
Fuzzy Clustering Algorithm ◽  
X Ray Computed ◽  
Effectiveness And Efficiency

The existing x-ray computed tomography algorithm simulation assume the complete measurements of the investigated objectives to be available, but this is not true in most applications. To overcome the problem, we creatively propose a method of image reconstruction based on fuzzy clustering algorithm under limited measurements. Different from the existing algorithms, we map all measurements into a set of vectors and cluster all vectors for the image reconstruction. The proposed algorithm aims to be easily realized, lower time complexity, and applicable in a real-time manner in case of limited measurements of the investigated objectives. Experiments demonstrate the effectiveness and efficiency of the proposed algorithm.

scholarly journals Hybrid Measuring the Similarity Value Based on Genetic Algorithm for Improving Prediction in A Collaborative Filtering Recommendation System.

2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Muaadh Abdo Mohammed Ahmed AL sabri
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Forecast Accuracy ◽  
Similarity Measures ◽  
Nearest Neighbors ◽  
Personalized Recommendation ◽  
Education Economics ◽  
Standard Methods ◽  
Wide Range ◽  
Suitable Product

In recent years, the Recommendation System (RS) has a wide range of applications in several fields, like Education, Economics, Scientific Researches and other related fields. The Personalized Recommendation is effective in increasing RS's accuracy, based on the user interface, preferences and constraints seek to predict the most suitable product or services. Collaborative Filtering (CF) is one of the primary applications that researchers use for the prediction of the accuracy rating and recommendation of objects. Various experts in the field are using methods like Nearest Neighbors (NN) based on the measures of similarity.  However, similarity measures use only the co-rated item ratings while calculating the similarity between a pair of users or items. The two standard methods used to measure similarities are Cosine Similarity (CS) and Person Correlation Similarity (PCS). However, both are having drawbacks, and the present piece of the investigation will approach through the optimized Genetic Algorithms (GA) to improve the forecast accuracy of RS using the merge output of CS with PCS based on GA methods. The results show GA's superiority and its ability to achieve more correct predictions than CS and PCS.

Research on Location-Based Personalized Recommendation System

2014 ◽  
Vol 490-491 ◽  
pp. 1493-1496
Author(s):  
Huan Gao ◽  
Xi Tian ◽  
Xiang Ling Fu
Keyword(s):  
Collaborative Filtering ◽  
Real Time ◽  
Recommendation System ◽  
Information Overload ◽  
Information Filtering ◽  
Mobile Internet ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
New System

With the mobile Internet developing in China, the problem of information overload has been brought to us. The traditional personalized recommendation cannot meet the needs of the mobile Internet. In this paper, the recommendation algorithm is mainly based on the collaborative filtering, but the new factors are introduced into the recommendation system. The new system takes the user's location and friends recommendation into the personalized recommendation system so that the recommendation system can meet the mobile Internet requirements. Besides, this paper also puts forward the concept of moving business circle for information filtering, which realizes the precise and real-time personalized recommendations. This paper also proves the recommendation effects through collecting and analyzing the data, which comes from the website of dianping.com.

scholarly journals A 64-Line Lidar-Based Road Obstacle Sensing Algorithm for Intelligent Vehicles

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Hai Wang ◽  
Xinyu Lou ◽  
Yingfeng Cai ◽  
Long Chen
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Good Accuracy ◽  
Intelligent Vehicles ◽  
Svm Classifier ◽  
Distance Threshold ◽  
The Road ◽  
Time Performance ◽  
Motion State ◽  
Time Requirements

Based on the 64-line lidar sensor, an object detection and classification algorithm with both effectiveness and real time is proposed. Firstly, a multifeature and multilayer lidar points map is used to separate the road, obstacle, and suspension object. Then, obstacle grids are clustered by a grid-clustering algorithm with dynamic distance threshold. After that, by combining the motion state information of two adjacent frames, the clustering results are corrected. Finally, the SVM classifier is used to classify obstacles with clustered object position and attitude features. The good accuracy and real-time performance of the algorithm are proved by experiments, and it can meet the real-time requirements of the intelligent vehicles.

Research on Multiple Cases Database Construction of Case-Based Reasoning Personalized Recommendation System

2013 ◽  
Vol 303-306 ◽  
pp. 1448-1451
Author(s):  
Jie Li Sun ◽  
Yun Lu ◽  
Fu Liang Li
Keyword(s):  
Recommendation System ◽  
Construction Method ◽  
Personalized Recommendation ◽  
Case Based Reasoning ◽  
Case Method ◽  
Database Construction ◽  
Case Based

The multiple cases database construction is one of the important links to design the personalized recommendation system. Personalized recommendation system case can be organized with multiple cases database based on expert experience and thinking patterns, combined with the traditional case method of organization. This paper studies the multiple cases database construction method of the personalized recommendation system based-CBR.

scholarly journals The application of e-commerce recommendation system in smart cities based on big data and cloud computing

2021 ◽  
pp. 26-26
Author(s):  
Yiman Zhang
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Collaborative Filtering ◽  
Real Time ◽  
Evaluation System ◽  
Recommendation System ◽  
Smart Cities ◽  
Personalized Recommendation ◽  
Current Recommendation ◽  
Recommendation Algorithm

In the era of big data, the amount of Internet data is growing explosively. How to quickly obtain valuable information from massive data has become a challenging task. To effectively solve the problems faced by recommendation technology, such as data sparsity, scalability, and real-time recommendation, a personalized recommendation algorithm for e-commerce based on Hadoop is designed aiming at the problems in collaborative filtering recommendation algorithm. Hadoop cloud computing platform has powerful computing and storage capabilities, which are used to improve the collaborative filtering recommendation algorithm based on project, and establish a comprehensive evaluation system. The effectiveness of the proposed personalized recommendation algorithm is further verified through the analysis and comparison with some traditional collaborative filtering algorithms. The experimental results show that the e-commerce system based on cloud computing technology effectively improves the support of various recommendation algorithms in the system environment; the algorithm has good scalability and recommendation efficiency in the distributed cluster, and the recommendation accuracy is also improved, which can improve the sparsity, scalability and real-time problems in e-commerce personalized recommendation. This study greatly improves the recommendation performance of e-commerce, effectively solves the shortcomings of the current recommendation algorithm, and further promotes the personalized development of e-commerce.

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