Recommendation of scholarly venues based on dynamic user interests
TLDR
A way to recommend relevant, specialized scholarly venues using these implicit ratings that can provide quick results, even for new researchers without a publication history and for emerging scholarly venues that do not yet have an impact factor is presented.About:
This article is published in Journal of Informetrics.The article was published on 2017-05-01 and is currently open access. It has received 41 citations till now. The article focuses on the topics: Scholarly communication & Altmetrics.read more
Citations
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Journal ArticleDOI
Academic social networks: Modeling, analysis, mining and applications
TL;DR: This study investigates the background, the current status, and trends of academic social networks, and systematically review representative research tasks in this domain from three levels: actor, relationship, and network.
Journal ArticleDOI
A hybrid personalized scholarly venue recommender system integrating social network analysis and contextual similarity
Tribikram Pradhan,Sukomal Pal +1 more
TL;DR: This work provides an integrated framework incorporating social network analysis, including centrality measure calculation, citation and co-citation analysis, topic modeling based contextual similarity, and key-route identification based main path analysis of a bibliographic citation network.
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Early indicators of scientific impact: Predicting citations with altmetrics
TL;DR: In this paper, the authors used various classification and regression models and evaluated their performance, finding neural networks and ensemble models to perform best for these tasks, finding that Mendeley readership is the most important factor in predicting the early citations, followed by other factors such as the academic status of the readers (e.g., student, postdoc, professor), followers on Twitter, online post length, author count, and the number of mentions on Wikipedia, and across different countries.
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CNAVER: A Content and Network-based Academic VEnue Recommender system
Tribikram Pradhan,Sukomal Pal +1 more
TL;DR: CNAVER is proposed, an integrated framework employing a rank-based fusion of paper-paper peer network model and venue-venue peer network (VVPN) model that only requires the title and abstract of a paper to provide venue recommendations, thus assisting researchers even at the earliest stage of paper writing.
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CLAVER: An integrated framework of convolutional layer, bidirectional LSTM with attention mechanism based scholarly venue recommendation
TL;DR: CLAVERG??an integrated framework of Convolutional Layer, bi-directional LSTM with an Attention mechanism-based scholarly VEnue Recommender system is presented, the first of its kind to integrate multiple deep learning-based concepts.
References
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Journal ArticleDOI
Matrix Factorization Techniques for Recommender Systems
TL;DR: As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.
Proceedings ArticleDOI
Item-based collaborative filtering recommendation algorithms
TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Journal ArticleDOI
Evaluating collaborative filtering recommender systems
TL;DR: The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole.
Proceedings ArticleDOI
GroupLens: an open architecture for collaborative filtering of netnews
TL;DR: GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles, and protect their privacy by entering ratings under pseudonyms, without reducing the effectiveness of the score prediction.
Journal ArticleDOI
Cumulated gain-based evaluation of IR techniques
TL;DR: This article proposes several novel measures that compute the cumulative gain the user obtains by examining the retrieval result up to a given ranked position, and test results indicate that the proposed measures credit IR methods for their ability to retrieve highly relevant documents and allow testing of statistical significance of effectiveness differences.