scholarly journals A Study on Publication Recommender System with Content Modelling

2020 ◽  
pp. 399-403
Author(s):  
Htay Htay Win
Keyword(s):  
Social Network ◽  
Dimensionality Reduction ◽  
Correspondence Analysis ◽  
Recommender System ◽  
Topic Modelling ◽  
Research Papers ◽  
The Right

For years, achievements and discoveries made by researcher are made aware through research papers published in appropriate journals or conferences. Many a time, established s researcher and mainly new user are caught up in the predicament of choosing an appropriate conference to get their work all the time. Every scienti?c conference and journal is inclined towards a particular ?eld of research and there is a extensive group of them for any particular ?eld. Choosing an appropriate venue is needed as it helps in reaching out to the right listener and also to further one’s chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of receipt. We present three di?erent approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling. In all these approaches, we apply Correspondence Analysis (CA) to obtain appropriate relationships between the entities in question, such as conferences and papers. Our models show hopeful results when compared with existing methods such as content-based ?ltering, collaborative ?ltering and hybrid ?ltering.

scholarly journals Analysis on Research Paper Publication Recommender System with Authors - Conferences Matrix

2020 ◽  
pp. 195-202
Author(s):  
Htay Htay Win
Keyword(s):  
Social Network ◽  
Dimensionality Reduction ◽  
Correspondence Analysis ◽  
Recommender System ◽  
Research Paper ◽  
Topic Modelling ◽  
Research Papers ◽  
The Right

For years, achievements and discoveries made by researcher are made aware through research papers published in appropriate journals or conferences. Many a time, established s researcher and mainly new user are caught up in the predicament of choosing an appropriate conference to get their work all the time. Every scienti?c conference and journal is inclined towards a particular ?eld of research and there is a extensive group of them for any particular ?eld. Choosing an appropriate venue is needed as it helps in reaching out to the right listener and also to further one’s chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of receipt. We present three di?erent approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling. In all these approaches, we apply Correspondence Analysis (CA) to obtain appropriate relationships between the entities in question, such as conferences and papers. Our models show hopeful results when compared with existing methods such as content-based ?ltering, collaborative ?ltering and hybrid ?ltering.

Analysis on Research Paper Publication Recommendation System with Composition of Papers and Conferences Matrices

2020 ◽  
pp. 120-127
Author(s):  
Htay Htay Win ◽  
Aye Thida Myint ◽  
Mi Cho Cho
Keyword(s):  
Social Network ◽  
Dimensionality Reduction ◽  
Correspondence Analysis ◽  
Recommendation System ◽  
Research Paper ◽  
Topic Modelling ◽  
Research Papers ◽  
The Right ◽  
Publication Recommendation

For years, achievements and discoveries made by researcher are made aware through research papers published in appropriate journals or conferences. Many a time, established s researcher and mainly new user are caught up in the predicament of choosing an appropriate conference to get their work all the time. Every scienti?c conference and journal is inclined towards a particular ?eld of research and there is a extensive group of them for any particular ?eld. Choosing an appropriate venue is needed as it helps in reaching out to the right listener and also to further one’s chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of receipt. We present three di?erent approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling. In all these approaches, we apply Correspondence Analysis (CA) to obtain appropriate relationships between the entities in question, such as conferences and papers. Our models show hopeful results when compared with existing methods such as content-based ?ltering, collaborative ?ltering and hybrid ?ltering.

scholarly journals Recommendation System

2021 ◽  
Vol 6 (12) ◽  
pp. 484-492
Author(s):  
Dr. ML Sharma C Vinay Kumar Saini and Jai Raj Singh
Keyword(s):  
Recommender System ◽  
Recommendation System ◽  
Research Paper ◽  
Research Field ◽  
Collaborative Approach ◽  
Research Papers ◽  
Area Of Interest ◽  
Overall Performance ◽  
The Right ◽  
Filtering Approach

Research paper recommenders emerged over the last decade to ease finding publications relating to researchers’ area of interest. The challenge was not just to provide researchers with very rich publications at any time, any place and in any form but to also offer the right publication to the right researcher in the right way. Several approaches exist in handling paper recommender systems. However, these approaches assumed the availability of the whole contents of the recommending papers to be freely accessible, which is not always true due to factors such as copyright restrictions. This paper presents a collaborative approach for research paper recommender system. By leveraging the advantages of collab- orative filtering approach, we utilize the publicly available contextual metadata to infer the hidden associations that exist between research papers in order to personalize recommen- dations. The novelty of our proposed approach is that it provides personalized recommen- dations regardless of the research field and regardless of the user’s expertise. Using a publicly available dataset, our proposed approach has recorded a significant improvement over other baseline methods in measuring both the overall performance and the ability to return relevant and useful publications at the top of the recommendation list.

Unsupervised dimensionality reduction for very large datasets: Are we going to the right direction?

2020 ◽  
Vol 196 ◽  
pp. 105777
Author(s):  
Jadson Jose Monteiro Oliveira ◽  
Robson Leonardo Ferreira Cordeiro
Keyword(s):  
Dimensionality Reduction ◽  
Large Datasets ◽  
Very Large Datasets ◽  
The Right

scholarly journals Elizabeth Wager. GETTING RESEARCH PUBLISHED – An A to Z of Publication Strategy, Third Edition. Boca Raton, New York, London: CRC Press, Taylor & Francis Group, LLC, 2015. 188 pages; ISBN-13:978-1-78523-138-4 (Paperback) - CAT# K28669

2016 ◽  
Vol 4 (1) ◽  
pp. 185-186
Author(s):  
Doncho Donev
Keyword(s):  
New York ◽  
Scientific Paper ◽  
Research Papers ◽  
Essential Information ◽  
Publication Strategy ◽  
Good Publication ◽  
Scientific Papers ◽  
Publishing Strategy ◽  
The Right ◽  
User Friendly

PURPOSE: This book provides step-by-step guidance on developing a sound publication strategy for how to prepare and get research papers published. The book is a user-friendly guide, a route map for publishing that covers many topics, ranging from abstracts and blogs, tables and trial registration to ethical principles and conventions for writing scientific papers. Publishing the results of scientific research in the form of a scientific paper is the ultimate goal and the final stage of the research of each scientist. To write and publish papers is never going to be an easy task. With this book as their guide, researchers will be better informed and therefore should have an easier and altogether more pleasant path to publication with clear direction on how to choose the right journal, avoid publication delays, and resolve authorship disputes and many other problems associated with scientific publishing.CONTENTS: The 188 pages of the book are distributed in 5 chapters in Part I and 249 entries ordered by the letters of Alphabet in Part II creating an A to Z of publication strategy. In the Appendices there are four sections covering further reading, organizations, guidelines and principles of good publication practice for company-sponsored medical research. The book also contains key references and useful websites within many entries where it seemed helpful. The last ten pages of the book present an index to help users to find the information of interest in the book.CONCLUSION: The book is intended to help all authors, young and old, novice and experienced, to plan their research and publications effectively and prepare manuscripts for journals and other publications, increasing the likelihood that their work will be published. Providing essential information on publishing strategy and process, the book should be extremely useful to everyone who wants to publish research results.

A Prospect on an Intelligent Recommender System

2021 ◽  
Vol 12 (2) ◽  
pp. 25-43
Author(s):  
Pooja ◽  
Vishal Bhatnagar
Keyword(s):  
Recommender System ◽  
User Satisfaction ◽  
Recommendation System ◽  
Intelligent System ◽  
Literature Survey ◽  
Research Papers ◽  
Online Data ◽  
Principle Component ◽  
The Future

User satisfaction is the principle component in the prosperity of a recommender system to provide an exact recommendation within a rational amount of time. The recommendation system is an intelligent system that analyzes the large quantity of online data to predict the patterns. In this paper, various recommendation techniques are described as a literature survey and their classifications are explained based upon the attributes and characteristics required for the recommendation process. The categorization of the recommendation system hinge on the analysis of the research papers and identifies the areas of the future for the development of an intelligent system.

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