@inproceedings{GippML15,
	address = {Newport Beach, California},
	title = {{CITREC}: {An} {Evaluation} {Framework} for {Citation}-{Based} {Similarity} {Measures} based on {TREC} {Genomics} and {PubMed} {Central}},
	url = {paper=https://www.gipp.com/wp-content/papercite-data/pdf/gipp15b.pdf code=https://doi.org/10.5281/zenodo.3598371 data=https://doi.org/10.5281/zenodo.3598421},
	doi = {10.5281/zenodo.3547372},
	abstract = {Citation-based similarity measures such as Bibliographic Coupling and Co-Citation are an integral component of many information retrieval systems. However, comparisons of the strengths and weaknesses of measures are challenging due to the lack of suitable test collections. This paper presents CITREC, an open evaluation framework for citation-based and text-based similarity measures. CITREC prepares the data from the PubMed Central Open Access Subset and the TREC Genomics collection for a citation-based analysis and provides tools necessary for performing evaluations of similarity measures. To account for different evaluation purposes, CITREC implements 35 citation-based and text-based similarity measures, and features two gold standards. The first gold standard uses the Medical Subject Headings (MeSH) thesaurus and the second uses the expert relevance feedback that is part of the TREC Genomics collection to gauge similarity. CITREC additionally offers a system that allows creating user defined gold standards to adapt the evaluation framework to individual information needs and evaluation purposes.},
	booktitle = {Proceedings of the {iConference}},
	author = {Gipp, Bela and Meuschke, Norman and Lipinski, Mario},
	month = mar,
	year = {2015},
	keywords = {Literature Recommendation},
}

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However, comparisons of the strengths and weaknesses of measures are challenging due to the lack of suitable test collections. This paper presents CITREC, an open evaluation framework for citation-based and text-based similarity measures. CITREC prepares the data from the PubMed Central Open Access Subset and the TREC Genomics collection for a citation-based analysis and provides tools necessary for performing evaluations of similarity measures. To account for different evaluation purposes, CITREC implements 35 citation-based and text-based similarity measures, and features two gold standards. The first gold standard uses the Medical Subject Headings (MeSH) thesaurus and the second uses the expert relevance feedback that is part of the TREC Genomics collection to gauge similarity. CITREC additionally offers a system that allows creating user defined gold standards to adapt the evaluation framework to individual information needs and evaluation purposes.","booktitle":"Proceedings of the iConference","author":[{"propositions":[],"lastnames":["Gipp"],"firstnames":["Bela"],"suffixes":[]},{"propositions":[],"lastnames":["Meuschke"],"firstnames":["Norman"],"suffixes":[]},{"propositions":[],"lastnames":["Lipinski"],"firstnames":["Mario"],"suffixes":[]}],"month":"March","year":"2015","keywords":"Literature Recommendation","bibtex":"@inproceedings{GippML15,\n\taddress = {Newport Beach, California},\n\ttitle = {{CITREC}: {An} {Evaluation} {Framework} for {Citation}-{Based} {Similarity} {Measures} based on {TREC} {Genomics} and {PubMed} {Central}},\n\turl = {paper=https://www.gipp.com/wp-content/papercite-data/pdf/gipp15b.pdf code=https://doi.org/10.5281/zenodo.3598371 data=https://doi.org/10.5281/zenodo.3598421},\n\tdoi = {10.5281/zenodo.3547372},\n\tabstract = {Citation-based similarity measures such as Bibliographic Coupling and Co-Citation are an integral component of many information retrieval systems. 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