Recommendation System with Association Rule Mining and Mobile Augmented Reality

2019 ◽  
Author(s):  
Kokten Ulas Birant ◽  
Pelin Yildirim Taser ◽  
Halil Ozgen Dindar ◽  
Derya Birant
Keyword(s):  
Augmented Reality ◽  
Association Rule ◽  
Association Rule Mining ◽  
Recommendation System ◽  
Mobile Augmented Reality ◽  
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 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.

scholarly journals Pemodelan personalisasi rekomendasi buku dengan pendekatan association rule mining

2018 ◽  
Vol 14 (2) ◽  
pp. 238
Author(s):  
Sely Yoanda ◽  
Imas Sukaesih Sitanggang ◽  
Agus Buono
Keyword(s):  
Association Rule ◽  
Web Application ◽  
Association Rule Mining ◽  
Recommendation System ◽  
Academic Library ◽  
Public Access ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Collection Method ◽  
E Mail

Introduction. Library X is an academic library in Jakarta, Indonesia. Library X has provided Online Public Access Catalog (OPAC) as a tool to provide information related to the collection. However, sometimes the information appears does not show high relevancy. One way to solve this problem is to develop user need based-book recommendation system. The purpose of this study is to create personalization model of book recommendations in Library X.Data Collection Method. The method used in this study was association rule mining using Apriori algorithm. Results and Discussions. The results showed that the book relationships for the minimum support was 0.1% and the minimum confidence was 10% and generated 42 association rules. It is noted that 657 (Accounting) and 658 (Management) are found to support for 2.6% with the confidence level for 14%.Conclusions. Book recommendation is formulated by selecting the rule with maximum support and confidence. The recommendation system is designed to be integrated to web application and user’s e-mail.

Sign in / Sign up

Export Citation Format

Share Document