Enhanced context-aware citation recommendation with auxiliary textual information based on an auto-encoding mechanism

Article Computer Science, Artificial Intelligence

Global-local neighborhood based network representation for citation recommendation

Xiaoyan Cai et al.

Summary: This study proposes a network representation model called GLNNR, which integrates global and local neighborhoods to obtain better node representation in the network. Empirical experiments prove the effectiveness of GLNNR.

APPLIED INTELLIGENCE (2022)

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Article Computer Science, Information Systems

Bot2Vec: A general approach of intra-community oriented representation learning for bot detection in different types of social networks

Phu Pham et al.

Summary: Due to the rapid growth of online social networks, the number of machine accounts/social bots has increased, prompting the need for reliable bot detection mechanisms to keep OSNs safe. The proposed Bot2Vec model utilizes network representation learning to automatically retain local neighborhood relations and user community structures, without relying on user profile features. By employing intra-community random walk strategy, Bot2Vec aims to outperform existing network embedding baselines for bot detection tasks. Extensive experiments on Twitter and Tagged networks demonstrate the effectiveness of the proposed model.

INFORMATION SYSTEMS (2022)

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Article Business

Deep learning based dual encoder retrieval model for citation recommendation

Fang Da et al.

Summary: This paper proposes a deep-learning-based dual encoder retrieval model for improving the performance of citation recommendation. It encodes the input query and paper titles into semantic vectors, matches them to compute similarity scores, and generates a sorted list of documents.

TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE (2022)

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Article Computer Science, Information Systems

VOPRec: Vector Representation Learning of Papers with Text Information and Structural Identity for Recommendation

Xiangjie Kong et al.

Summary: In the era of scholarly big data, finding relevant papers is a challenging task for scholars. A scientific paper recommendation system called VOPRec has been developed to recommend relevant papers to scholars by leveraging recent research in text and network representation learning. VOPRec outperforms state-of-the-art paper recommendation baselines in terms of precision, recall, F1, and NDCG, as demonstrated through the APS data set.

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING (2021)

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Article Computer Science, Artificial Intelligence

A context-aware recommendation approach based on feature selection

Lei Chen et al.

Summary: This paper proposes a context-aware recommendation approach based on embedded feature selection, which effectively addresses the issues of data sparsity and context redundancy by generating a minimum subset and allocating weights appropriately, demonstrating better performance in restaurant recommendation experiments.

APPLIED INTELLIGENCE (2021)

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Article Computer Science, Artificial Intelligence

Venue Topic Model-enhanced Joint Graph Modelling for Citation Recommendation in Scholarly Big Data

Wei Wang et al.

Summary: This paper proposes a joint graph modeling approach enhanced with author topic modeling to address the problem of overlooking multiple relationships in venue recommendation. Experimental results show that the proposed method outperforms state-of-the-art methods.

ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING (2021)

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Proceedings Paper Computer Science, Artificial Intelligence

C-Rex: A Comprehensive System for Recommending In-Text Citations with Explanations

Michael Faerber et al.

Summary: C-REx is a context-aware citation recommendation system based on the Neural Citation Network and millions of publications from the Microsoft Academic Graph. It is the first online citation recommendation system that incorporates both deep learning and explanation components. Offline evaluation shows promising performance, and online evaluation demonstrates that explanations of recommendations increase user satisfaction.

WEB CONFERENCE 2021: COMPANION OF THE WORLD WIDE WEB CONFERENCE (WWW 2021) (2021)

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Article Computer Science, Artificial Intelligence

Influential Performance of Nodes Identified by Relative Entropy in Dynamic Networks

Peter Marjai et al.

Summary: The study explores the identification of influential nodes in dynamic networks using the TOPSIS method combined with relative entropy, evaluating the average infection capacity, Time-Constrained Coverage, and Cover Time of ranked nodes.

VIETNAM JOURNAL OF COMPUTER SCIENCE (2021)

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Article Computer Science, Information Systems