An Effective Academic Research Papers Recommendation for Non-profiled Users

Hanyurwimfura, Damien; Liu, Bo; Havyarimana, Vincent; Njagi, Dennis; Kagorora, Faustin

doi:10.14257/ijhit.2015.8.3.23

Cited by 15 publications

(9 citation statements)

References 21 publications

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“…In 2015, Hanyurwimfura et al [7] proposed a method for recommending academic research papers to researchers without relying on user profiles. Traditional user profile-based systems necessitate user registration and recommend papers based on profile similarity.…”

Section: Content-based Recommender Systemmentioning

confidence: 99%

“…(6) The data preparation and learning module retrieves articles from the search articles buffer and prepares them for training the learning model. (7) The trained model is then sent from the data preparation and learning module to the recommendation module. (8) The recommendation module utilizes the trained model to predict relevant articles based on the user's paper.…”

Section: Sequence Diagrammentioning

confidence: 99%

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An Autonomous Multi-Agent System for Customized Scientific Literature Recommendation: A Tool for Researchers and Students

Abdelkader¹

2023

ISI

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The ever-accelerating growth in scientific literature presents a formidable challenge for researchers and students aiming to stay abreast of the most recent findings. Previous solutions, which include content-based, collaborative-based, and graph-based filtering recommendation systems, have their own limitations, primarily their inability to efficiently manage time-consuming search engine queries, a frequent issue for students. To address these constraints, we introduce a novel tool-a multi-agent system with an intelligent filtering mechanism. This system automates the literature search and filtering process, generating search queries independently and conducting comprehensive online searches. The system comprises autonomous agents that collectively gather and analyze data from a myriad of sources. Utilizing sophisticated techniques, the intelligent filtering mechanism leverages user preferences, interests, and contextual information. Continuous learning from user feedback allows the system to iteratively refine its recommendations, providing a personalized user experience. A user-friendly interface has been developed to streamline the configuration of the search procedure, offering users an easy way to fine-tune their preferences. Evaluations indicate that our approach delivers superior performance, significantly improving the process of scientific literature recommendation. Our tool is designed to assist researchers and students by minimizing the manual effort required in literature search and filtering, thereby ensuring efficient access to pertinent information. By automating these labor-intensive tasks, our tool enables users to keep pace with the latest scientific discoveries with increased ease.

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Section: Content-based Recommender Systemmentioning

confidence: 99%

Section: Sequence Diagrammentioning

confidence: 99%

An Autonomous Multi-Agent System for Customized Scientific Literature Recommendation: A Tool for Researchers and Students

Abdelkader¹

2023

ISI

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“…All these introduced profile learning methods are relying on researchers' historical records or actions. In some recommender systems, they regard the papers provided by the researcher as input to build user profile [43], [44]. After the paper is provided, the needed information for the system will be extracted from the paper's title, introduction, related work, conclusion, references part to determine the user's profile.…”

Section: ) Profile Learningmentioning

confidence: 99%

Scientific Paper Recommendation: A Survey

et al. 2019

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Globally, the recommendation services have become important due to the fact that they support e-commerce applications and different research communities. Recommender systems have a large number of applications in many fields, including economic, education, and scientific research. Different empirical studies have shown that the recommender systems are more effective and reliable than the keyword-based search engines for extracting useful knowledge from massive amounts of data. The problem of recommending similar scientific articles in scientific community is called scientific paper recommendation. Scientific paper recommendation aims to recommend new articles or classical articles that match researchers' interests. It has become an attractive area of study since the number of scholarly papers increases exponentially. In this paper, we first introduce the importance and advantages of the paper recommender systems. Second, we review the recommendation algorithms and methods, such as Content-based, collaborative filtering, graph-based, and hybrid methods. Then, we introduce the evaluation methods of different recommender systems. Finally, we summarize the open issues in the paper recommender systems, including cold start, sparsity, scalability, privacy, serendipity, and unified scholarly data standards. The purpose of this survey is to provide comprehensive reviews on the scholarly paper recommendation.

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“…In 2015, Hanyurwimfura [13] proposed a citation recommendation systems for non-profile users. His methodology was helpful to new users having no data to compensate for their profile.…”

Section: Literature Reviewmentioning

confidence: 99%

Centrality-Based Paper Citation Recommender System

Samad¹,

Islam²,

Iqbal³

et al. 2019

EAI Endorsed Transactions on Industrial Networks and Intelligen

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Researchers cite papers in order to connect the new research ideas with previous research. For the purpose of finding suitable papers to cite, researchers spend a considerable amount of time and effort. To help researchers in finding relevant/important papers, we evaluated textual and topological similarity measures for citation recommendations. This work analyzes textual and topological similarity measures (i.e., Jaccard and Cosine) to evaluate which one performs well in finding similar papers? To find the importance of papers, we compute centrality measures (i.e., Betweeness, Closeness, Degree and PageRank). After evaluation, it is found that topological-based similarity via Cosine achieved 85.2% and using Jaccard obtained 61.9% whereas textualbased similarity via Cosine on abstract obtained 68.9% and using Cosine on title achieved 37.4% citation links. Likewise, textual-based similarity via Jaccard on abstract obtained 35.4% and using Jaccard on title achieved 28.3% citation links.

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An Effective Academic Research Papers Recommendation for Non-profiled Users

Abstract: Abstract

Cited by 15 publications

References 21 publications

An Autonomous Multi-Agent System for Customized Scientific Literature Recommendation: A Tool for Researchers and Students

An Autonomous Multi-Agent System for Customized Scientific Literature Recommendation: A Tool for Researchers and Students

Scientific Paper Recommendation: A Survey

Centrality-Based Paper Citation Recommender System

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