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Cross-Domain Citation Recommendation Based on Topic Model and Co-Citation Selection

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dc.contributor.advisor Guha, Sumanta
dc.contributor.author Tantanasiriwong, Supaporn
dc.contributor.other Janecek, Paul
dc.contributor.other Haruechaiyasak, Choochart
dc.date.accessioned 2017-08-15T07:33:29Z
dc.date.available 2017-08-15T07:33:29Z
dc.date.issued 2017-08
dc.identifier.uri http://www.cs.ait.ac.th/xmlui/handle/123456789/873
dc.description.abstract Finding publications for citation is a crucial task in the research community. Experienced researchers may indeed find it easy to determine relevant papers from a single source domain such as a publication database. However, the task is significantly more challenging when considering publications across different domain sources. In this dissertation, we propose a Cross-Domain Recommender System as a solution to the task. Our recommender system implements an algorithm which is a hybridization of two distinct approaches – the Topic Model and Co-Citation Selection approaches. Briefly, relevant terms from documents are first clustered into similar topics. The Co-Citation Selection technique then helps select citations based on a set of highly similar documents. To evaluate the performance, various comparison techniques are introduced to the Cross-Domain Citation Recommendations (CDCR). We focus on the context of patent and publication as a primary and secondary domain, respectively. The outcome of our proposed cross-domain citation framework is confirmed through benchmarking with traditional baseline approaches using a corpus of patents collected from different technological fields, e.g., biotechnology, environmental technology, medical technology, and nanotechnology. Experimental results show that our hybrid algorithm approach yields better performance in predicting relevant publication citations than the known baseline approaches. en_US
dc.description.sponsorship Ministry of Science and Technology National Science and Technology Development Agency (NSTDA), Thailand en_US
dc.language.iso en_US en_US
dc.publisher AIT en_US
dc.subject cross domain recommender system; citation recommendation; cross domain citation recommendation; topic model; co-citation selection; information retrieval; keyphrase extraction tool; similarity measures; evaluation; ANOVA; analysis of variance. en_US
dc.title Cross-Domain Citation Recommendation Based on Topic Model and Co-Citation Selection en_US
dc.type Dissertation en_US

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