Cross-domain citation recommendation based on hybrid topic model and co-citation selection citation selection

Tantanasiriwong, Supaporn and Guha, Sumanta and Janecek, Paul and Haruechaiyasak, Choochart and Azzopardi, Leif (2017) Cross-domain citation recommendation based on hybrid topic model and co-citation selection citation selection. International Journal of Data Mining, Modelling and Management, 9 (3). pp. 220-236. ISSN 1759-1163 (https://doi.org/10.1504/IJDMMM.2017.086566)

[thumbnail of Tantanasiriwong-etal-DMMM-2017-Cross-domain-citation-recommendation-based-on-hybrid-topic-model]
Preview
 Text. Filename: Tantanasiriwong_etal_DMMM_2017_Cross_domain_citation_recommendation_based_on_hybrid_topic_model.pdf
Accepted Author Manuscript
Download (593kB)| Preview

Abstract

Cross-domain recommendations are of growing importance in the research community. An application of particular interest is to recommend a set of relevant research papers as citations for a given patent. This paper proposes an approach for cross-domain citation recommendation based on the hybrid topic model and co-citation selection. Using the topic model, relevant terms from documents could be clustered into the same topics. In addition, the co-citation selection technique will help select citations based on a set of highly similar patents. To evaluate the performance, we compared our proposed approach with the traditional baseline approaches using a corpus of patents collected for different technological fields of biotechnology, environmental technology, medical technology and nanotechnology. Experimental results show our cross domain citation recommendation yields a higher performance in predicting relevant publication citations than all baseline approaches.

  • Item type:Article
    ID code:62730
    Dates:
    Date
    Event
    13 September 2017
    Published
    13 September 2017
    Published Online
    1 June 2017
    Accepted
    Keywords:analysis of variance, Anova, ccs, cdcr, co-citation selection, cross domain citation recommendation, cross domain recommender system, evaluation, information retrieval, keyphrase extraction tool, similarity measures, topic model, Electronic computers. Computer science, Management Information Systems, Modelling and Simulation, Computer Science Applications
    Subjects:Science > Mathematics > Electronic computers. Computer science
    Department:Faculty of Science > Computer and Information Sciences
    Depositing user: Pure Administrator
    Date deposited:22 Dec 2017 14:04
    Last modified:25 May 2023 10:06
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/62730
CORE (COnnecting REpositories)