AI SearchCross-domain citation recommendation base on topic model and co-citation selection
Supapron Tantanasiriwong
Cross-domain citation recommendation base on topic model and co-citation selection - Pathum Thani, Thailand : Asian Institute of Technology, 2016 - 70 leaves : ill. - Dissertation ; no. IM-17-01 . - Asian Institute of Technology. Dissertation ; no. IM-17-01 .
A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Management
Thesis (Ph.D.) - Asian Institute of Technology, 2016
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 CrossDomain 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
Knowledge management
Information and retrieval systems
Database management
Cross-domain citation recommendation base on topic model and co-citation selection - Pathum Thani, Thailand : Asian Institute of Technology, 2016 - 70 leaves : ill. - Dissertation ; no. IM-17-01 . - Asian Institute of Technology. Dissertation ; no. IM-17-01 .
A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Management
Thesis (Ph.D.) - Asian Institute of Technology, 2016
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 CrossDomain 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
Knowledge management
Information and retrieval systems
Database management