Collaborative book recommendation system using trust based social network and association rule mining

2016 ◽  
Author(s):  
Anand Shanker Tewari ◽  
Asim Gopal Barman
Keyword(s):  
Social Network ◽  
Association Rule ◽  
Association Rule Mining ◽  
Recommendation System ◽  
Rule Mining

scholarly journals Code Edit Recommendation Using a Recurrent Neural Network

2021 ◽  
Vol 11 (19) ◽  
pp. 9286
Author(s):  
Seonah Lee ◽  
Jaejun Lee ◽  
Sungwon Kang ◽  
Jongsun Ahn ◽  
Heetae Cho
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Association Rule ◽  
Association Rule Mining ◽  
Recommendation System ◽  
Software Evolution ◽  
Rule Mining ◽  
Mining Technique ◽  
Recommendation Accuracy ◽  
The Given

When performing software evolution tasks, developers spend a significant amount of time looking for files to modify. By recommending files to modify, a code edit recommendation system reduces the developer’s navigation time when conducting software evolution tasks. In this paper, we propose a code edit recommendation method using a recurrent neural network (CERNN). CERNN forms contexts that maintain the sequence of developers’ interactions to recommend files to edit and stops recommendations when the first recommendation becomes incorrect for the given evolution task. We evaluated our method by comparing it with the state-of-the-art method MI-EA that was developed based on the association rule mining technique. The result shows that our proposed method improves the average recommendation accuracy by approximately 5% over MI-EA (0.64 vs. 0.59 F-score).

scholarly journals Recommendation system for web article based on association rules and topic modelling

2017 ◽  
Vol 1 (1) ◽  
pp. 26-33 ◽  
Author(s):  
Guntur Budi Herwanto ◽  
Annisa Maulida Ningtyas
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Recommendation System ◽  
Latent Dirichlet Allocation ◽  
Primary Source ◽  
Information Discovery ◽  
Rule Mining ◽  
Topic Interest ◽  
Content Recommendation ◽  
The Right

The World Wide Web is now the primary source for information discovery. A user visits websites that provide information and browse on the particular information in accordance   with their   topic interest.   Through  the  navigational process,  visitors  often  had  to  jump  over  the  menu  to  find  the right  content.  Recommendation system can help the visitors to find the right content immediately.  In this study, we propose a two-level recommendation system, based on association rule and topic similarity.  We generate association rule by applying Apriori algorithm.   The  dataset  for  association  rule  mining  is a  session of  topics  that  made  by  combining  the  result of  sessionization and  topic  modeling.  On  the  other   hand,   the  topic  similarity made  by  comparing   the  topic  proportion of  web  article.  This topic proportion inferred from the Latent Dirichlet Allocation (LDA). The results show that in our dataset there are not many interesting   topic relations in one session.  This  result  can  be resolved,  by  utilizing  the  second  level  of  recommendation  by looking into the article  that  has the similar  topic.

scholarly journals Application of Association Rule Mining and Social Network Analysis for Understanding Causality of Construction Defects

2019 ◽  
Vol 11 (3) ◽  
pp. 618 ◽  
Author(s):  
Sangdeok Lee ◽  
Yongwoon Cha ◽  
Sangwon Han ◽  
Changtaek Hyun
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Association Rule ◽  
Association Rule Mining ◽  
Cost Overrun ◽  
Rule Mining ◽  
Suggested Approach ◽  
Schedule Delay ◽  
Construction Defects

A construction defect can cause schedule delay, cost overrun and quality deterioration. In order to minimize these negative impacts of construction defects, this paper aims to analyze the causality of construction defects. Specifically, association rule mining (ARM) is used to quantify the interrelationships between defect causes, and social network analysis (SNA) is utilized to find out the most influential causes triggering generation of construction defects. The suggested approach was applied to 2949 defect instances in finishing work. Through this application, it was confirmed that the proposed approach can systematically identify and quantify causality among defect causes.

Defining the Factors that Effect User Interest on Social Network News Feeds via Fuzzy Association Rule Mining

2013 ◽  
pp. 334-345
Author(s):  
Basar Öztaysi ◽  
Sezi Çevik Onar
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Data Set ◽  
Frequent Items ◽  
Fuzzy Association Rule ◽  
Fuzzy Association Rule Mining

Social networking became one of the main marketing tools in the recent years since it’s a faster and cheaper way to reach the customers. Companies can use social networks for efficient communication with their current and potential customers but the value created through the usage of social networks depends on how well the organizations use these tools. Therefore a support system which will enhance the usage of these tools is necessary. Fuzzy Association rule mining (FARM) is a commonly used data mining technique which focuses on discovering the frequent items and association rules in a data set and can be a powerful tool for enhancing the usage of social networks. Therefore the aim of the chapter is to propose a fuzzy association rule mining based methodology which will present the potential of using the FARM techniques in the field of social network analysis. In order to reveal the applicability, an experimental evaluation of the proposed methodology in a sports portal will be presented.

scholarly journals Cluster Centric Technique for Association Rule Mining(C-ARM) For High Precision and Coverage in Web Page Recommendation

2019 ◽  
Vol 8 (6) ◽  
pp. 1732-1735
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Recommendation System ◽  
Computation Time ◽  
Web Pages ◽  
User Group ◽  
Web Page ◽  
Rule Mining ◽  
Modern Times ◽  
Over Time

Association Rule Mining (ARM) is known for its popularity and efficiency in the data mining domain. Over the recent years, the amount of data that gets accumulated in the internet is getting increased exponentially over time. The data available so are stored in online and are retrieved when a user requests for the same through key words with the help of a search engine. The important task of the search engines are to present the appropriate web pages that an user is expecting and in the modern times, The need of the hour is to recommend web pages to the users that he is interested in. This made the web page recommendation an important and vital task. Although many of the researchers are in the preliminary task of developing such systems, we in this research propose a recommendation model in which different users are interested upon a common item or domain by using the ARM concept. The data patterns that are in common are identified using the ARM and further these are clustered on a form of hierarchy .The clusters makes the recommendation system to easily identify the user group and based on the group, the pages are recommended, The experimental analysis are discussed and found to be efficient than the available methods in terms of computation time and reliability.

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