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Using citation-context to reduce topic drifting on pure citation-based recommendation

Khadka, Anita and Knoth, Petr (2018). Using citation-context to reduce topic drifting on pure citation-based recommendation. In: RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems, ACM Press pp. 362–366.

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Recent works in the area of academic recommender systems have demonstrated the effectiveness of co-citation and citation closeness in related-document recommendations. However, documents recommended from such systems may drift away from the main theme of the query document. In this work, we investigate whether incorporating the textual information in close proximity to a citation as well as the citation position could reduce such drifting and further increase the performance of the recommender system. To investigate this, we run experiments with several recommendation methods on a newly created and now publicly available dataset containing 53 million unique citation-based records. We then conduct a user-based evaluation with domain-knowledgeable participants. Our results show that a new method based on the combination of Citation Proximity Analysis (CPA), topic modelling and word embeddings achieves more than 20% improvement in Normalised Discounted Cumulative Gain (nDCG) compared to CPA.

Item Type: Conference or Workshop Item
Copyright Holders: 2018 The Authors
ISBN: 1-4503-5901-9, 978-1-4503-5901-6
Keywords: Recommender systems; citation proximity analysis; citation-context; word embeddings; topic modelling
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 56985
SWORD Depositor: Jisc Publications-Router
Depositing User: Jisc Publications-Router
Date Deposited: 09 Oct 2018 10:00
Last Modified: 08 Dec 2018 04:28
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