Journal Article

PUB-VEN: a personalized recommendation system for suggesting publication venues

Sarfraz, Muhammad Shahzad, Memon, Imran, Ajmal, Sahar, Alam, Khubaib Amjad, Bilal, Muhammad

2024

Overview

Researchers would like to publish their research articles in reputed journals along with quick review time. However, with the growing number of academic publications, it is becoming more difficult for scholars to find venues that are relevant to their domain. This study aims on the development of a technique that focuses on the priorities of the researchers that are linked to the recommendation of suitable suggestion of publication journal. The developed Recommendation System (RS) takes title, abstract, and keyword of the manuscript to be submitted. The proposed algorithm, named PUB-VEN which is hybridization of Content-Based Filtering (CBF), and Collaborative Filtering (CF), which is integrated with the Multi-Criteria Decision Making (MCDM) process to provide suitable journal recommendations by considering the researcher's point of view about different attributes gathered such as impact factor, eigen factor, average review time, etc. which affect the research process effectively. Our results demonstrate that the PUB-VEN provides better recommendations in comparison with state-of-the-art algorithms such as Term Frequency and Inverse Document Frequency (TF-IDF) and Latent Semantic Analysis (LSA). The study concluded that PUB-VEN is providing better precision, recall, F1 Score, Discounted Cumulative Gain (DCG), and Normalized DCG (NCDG). For precision, the gain ranges from 1% to 16%, the improvement in recall is between 33% and 3%, the betterment of result in F1 is by the ratio which ranges from 27% and 2%, the improvement in the result of DCG lies between 15% and 5% and the result of NDCG gain ranges from 6% to 1%. It is useful for the researchers in finding suitable venue for publication.

• Publisher: Springer US,Springer Nature B.V
• Subject: Algorithms / Computer Communication Networks / Computer Science / Data Structures and Information Theory / Documents / Filtration / Multimedia Information Systems / Multiple criterion / Recall / Recommender systems / Special Purpose and Application-Based Systems