A review of citation recommendation: from textual content to enriched context

Ma, Shutian; Zhang, Chengzhi; Liu, Xiaozhong

doi:10.1007/s11192-019-03336-0

Cited by 47 publications

(20 citation statements)

References 120 publications

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“…The other assumption is that users prefer patents similar to what they have already searched or purchased. Similar patents can be identified by random walking on different paths on patent citationbibliographic networks [16] and topics matching based on co-citation relations [26].…”

Section: Patent Recommendationmentioning

confidence: 99%

Leveraging Trust Relations to Improve Academic Patent Recommendation

Chen¹,

Ma²,

Zhu³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Academic patent trading is one of the important ways for university technology transfer. Compared to industry patent trading, academic patent trading suffers from a more serious information asymmetric problem. It needs a recommendation service to help companies identify academic patents that they want to pay. However, existing recommendation approaches have limitations in facilitating academic patent trading in online patent platforms because most of them only consider patent-level characteristics. A high trust degree of a company towards academic patents can alleviate the information asymmetry and encourage trading. This study proposes a novel academic patent recommendation approach with a hybrid strategy, combining citation-based relevance, connectivity, and trustworthiness. An offline experiment is conducted to evaluate the performance of the proposed recommendation approach. The results show that the proposed method performs better than the baseline methods in both accuracy and ranking.

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Section: Patent Recommendationmentioning

confidence: 99%

Leveraging Trust Relations to Improve Academic Patent Recommendation

Chen¹,

Ma²,

Zhu³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…The review focuses on studies investigating the relationship between cited and citing documents. The authors review studies published between 2006 and 2018 Ma et al (2020) Non-systematic The purpose of this review is to identify the methods and information used for citation-based recommendation systems Some studies have used deep learning classifiers such as Artificial Neural Network (ANN: inspired by biological neural networks, i.e., the human brain, and built to simulate humans' interconnected processes), Convolutional Neural Network (CNN: a special type of neural networks designed for cognitive tasks like image processing and NLP), Recurrent Neural Network (RNN: an improved variation of neural networks with a short-term memory to retain the contextual information from earlier results), and Long Short-Term Memory (LSTM: a variant of an RNN that uses the short-term memory of RNN neurons and makes them last longer).…”

Section: Systematicmentioning

confidence: 99%

A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies

et al. 2021

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“…The first important application is citation context-based summarization, which involves citation context extraction and selection. In detail, extracted citation contexts are classified into different clusters, a summary is generated by selecting citation contexts from each cluster (Ma et al, 2020) and diversification should be considered to reduce duplications in the recommended list. For example, Cohan and Goharian (2017) extracted citation contexts from a reference article for each citation and extracted candidate sentences for the summary by using the discourse facets of the citations as well as the community structure of the citation contexts.…”

Section: Research On Applications Of Citation Contextsmentioning

confidence: 99%

Exploring multiple diversification strategies for academic citation contexts recommendation

Chen

Yang²,

et al. 2020

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Purpose Citation contexts have been found useful in many scenarios. However, existing context-based recommendations ignored the importance of diversity in reducing the redundant issues and thus cannot cover the broad range of user interests. To address this gap, the paper aims to propose a novelty task that can recommend a set of diverse citation contexts extracted from a list of citing articles. This will assist users in understanding how other scholars have cited an article and deciding which articles they should cite in their own writing. Design/methodology/approach This research combines three semantic distance algorithms and three diversification re-ranking algorithms for the diversifying recommendation based on the CiteSeerX data set and then evaluates the generated citation context lists by applying a user case study on 30 articles. Findings Results show that a diversification strategy that combined “word2vec” and “Integer Linear Programming” leads to better reading experience for participants than other diversification strategies, such as CiteSeerX using a list sorted by citation counts. Practical implications This diversifying recommendation task is valuable for developing better systems in information retrieval, automatic academic recommendations and summarization. Originality/value The originality of the research lies in the proposal of a novelty task that can recommend a diversification context list describing how other scholars cited an article, thereby making citing decisions easier. A novel mixed approach is explored to generate the most efficient diversifying strategy. Besides, rather than traditional information retrieval evaluation, a user evaluation framework is introduced to reflect user information needs more objectively.

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A review of citation recommendation: from textual content to enriched context

Cited by 47 publications

References 120 publications

Leveraging Trust Relations to Improve Academic Patent Recommendation

Leveraging Trust Relations to Improve Academic Patent Recommendation

A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies

Exploring multiple diversification strategies for academic citation contexts recommendation

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