A Semantic Category Recommendation System Exploiting LDA Clustering Algorithm and Social Folksonomy

2015 ◽  
Author(s):  
Hyung-Rak Jo ◽  
Kyung-Wook Park ◽  
Jae-Ik Kim ◽  
Dong-Ho Lee
Keyword(s):  
Clustering Algorithm ◽  
Recommendation System ◽  
Semantic Category

scholarly journals Prediction of User Future Request Utilizing the Combination of Both ANN and FCM in Web Page Recommendation

2018 ◽  
Vol 29 (1) ◽  
pp. 583-595 ◽  
Author(s):  
V. Raju ◽  
N. Srinivasan
Keyword(s):  
Clustering Algorithm ◽  
Recommendation System ◽  
Model Data ◽  
Web Page ◽  
Third Phase ◽  
Consumer Access ◽  
User Data ◽  
Fuzzy C Means Clustering ◽  
Neural Network Algorithm ◽  
Projected Process

Abstract This paper explains about the web page recommendation system. This procedure encompasses consumers’ upcoming demand and web page recommendations. In the proposed web page recommendation system, potential and non-potential data can be categorized by use of the Levenberg–Marquardt firefly neural network algorithm, and forecast can be made by using the K-means clustering algorithm. Consequently, the projected representation demonstrates the infrequent contact format with the help of the representation that integrates the comparable consumer access model data that belong to the further consumer. Thereafter, the impending user data are specified to the clustering progression. The third phase of the projected process is collecting potential data with the aid of the improved fuzzy C-means clustering algorithm. The last step of our projected process is envisaging the upcoming demand for the subsequent consumer. The presentation of the projected procedure will be compared to the obtainable procedure.

scholarly journals Application of Data Mining to E-Commerce Recommendation Systems

2018 ◽  
Vol 7 (2.32) ◽  
pp. 420
Author(s):  
Dr P.V.R.D. Prasad Rao ◽  
S Varakumari ◽  
Vineetha B ◽  
V Satish
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Clustering Algorithm ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Information Storage ◽  
Apriori Algorithm ◽  
Rising Power

The rising power of technology has intensely improved the information storage, collection, and manipulation ability. As the information is growing very rapid along with its complexness, data analysis has become more important. The aim of this paper is to recommend products to the user which are more likely to be purchased. This paper, first describes about different techniques for recommendation and the research regarding recommendation system, then suggests a better approach for a good recommendation system and explains the results of that approach. Here, a combination of k-means clustering algorithm and apriori algorithm on transactional dataset so that a better recommendation list can be obtained. 

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.

scholarly journals A Personalized Web Based E-Learning Recommendation System To Enhance and User Learning Experience

2020 ◽  
Vol 9 (1) ◽  
pp. 1186-1195
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Recommendation System ◽  
User Behavior ◽  
Search Algorithm ◽  
Learning Experience ◽  
Research Work ◽  
Web Based ◽  
E Learning

The key aim of the data mining techniques is to help the user by reducing the effort for exploring the data, recovering the patterns, and implementing applications that help to find the knowledge specific contents, decision making, and predictions. This research work develops a recommendation system by using the merits of data mining algorithms. They are used for designing web-based e-learning recommendation systems. This model aims to understand the user behavior and contents requirements of the learner. This purpose is solved by obtaining the information from the data source and producing the suggestions of suitable content to the learner. The concept of web content mining and web usage mining has been combined together for performing the required work. This technique involves the genetic algorithm and k-means clustering algorithm for designing the presented model. In this work the k-means clustering algorithm has been used to track user behavior and the genetic algorithm has been used as a search algorithm to find the necessary resources in the database. Finally, the presented system is implemented and its performance is measured. The estimated results demonstrate that the presented model enhances the accuracy of recommendations and also speeds up the computations. A related performance calculation has also provided to justify this conclusion. The obtained results demonstrate that this technique is acceptable for new generation application designs

scholarly journals Facility Recommendation System Using Domination Set Theory in Graph

2019 ◽  
Vol 8 (9) ◽  
pp. 313-317
Keyword(s):  
Facility Location ◽  
Clustering Algorithm ◽  
Recommendation System ◽  
Location Problem ◽  
Transportation Cost ◽  
Facility Location Problem ◽  
Geographical Region ◽  
Infrastructure Development ◽  
Police Station

Facility location problem has gained importance with the increased applications involving infrastructure development which involves placing facilities in right positions. With the help of GPS based services the analysis of locations and traffic which is vital input for the problem of facility location. The problem of facility location recommender is a multi-objective problem of reducing the transportation cost and increasing the coverage in the geographical region. Conditions to place a facility for a better coverage and reduced cost will differ from facility to facility. For the purpose algorithms such as route finder, Fastest clustering algorithm are used to cluster geographical region for improved infrastructure and better Quality of Service. In this paper analyzes facility of locating schools, hospital and police station in a bounded geographical region. The algorithm uses domination set and k-means clustering algorithm to choose the facility and its corresponding cluster in the region. Clustered are validated using index measures including DBI and Dunn Index values. An experimental analysis is conducted for Coimbatore city and results are evaluated against real facilities.

scholarly journals Recommendation System of Information Technology Jobs using Collaborative Filtering Method Based on LinkedIn Skills Endorsement

SISFORMA ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. 63
Author(s):  
Latifah Diah Kumalasari ◽  
Ajib Susanto
Keyword(s):  
Information Technology ◽  
Collaborative Filtering ◽  
Engineering Students ◽  
Clustering Algorithm ◽  
Recommendation System ◽  
Software Engineer ◽  
It Professionals ◽  
Filtering Method ◽  
Skill Requirement ◽  
System Analyst

Students who are graduated from Informatics Engineering have wide employment opportunities in the information technology work field, such as database administrator, data scientist, UI designer, IT project manager, network engineer, system analyst, software engineer and UX designer. Each job in Information Technology field has different skill requirement for the interest of work field. Therefore, IT skill classification is needed to find out the suitable career recommendation for Informatics Engineering students. Data from IT professionals which are obtained from LinkedIn account of IT professionals will be processed as reference for students. Data are processed using K-Means Clustering algorithm to find out how is feasible IT professionals data are used as a reference. Then, Collaborative Filtering method by the K-NN algorithm is used to determine classification based on the proximity between student skills and information technology job field. The output is recommendation of information technology job field which are generated from calculate of IT student skills. Result has been tested by testing one of user that has been labeled software engineer produce a recommendation output as a software engineer.

scholarly journals Intelligent Recommendation System Based on Mathematical Modeling in Personalized Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yimin Cui
Keyword(s):  
Mathematical Modeling ◽  
Data Mining ◽  
Response Time ◽  
User Satisfaction ◽  
Clustering Algorithm ◽  
Recommendation System ◽  
Model Learning ◽  
Before And After ◽  
Purchase Rate ◽  
Click Through Rate

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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