Refereed full papers (journals, book chapters, international conferences)

1987

@article{JoF1987,
	vgclass =	{refpap},
	author =	{William P. Jones and George W. Furnas},
	title =	{Pictures of relevance: {A} geometric analysis of similarity
	measures},
	journal =	{Journal of the American Society for Information Science},
	volume =	{38},
	number =	{6},
	pages =	{420--442},
	year =	{1987},
	url =	{http://www3.interscience.wiley.com/cgi-bin/abstract/10017079/},
	abstract =	{We want computer systems that can help us assess the
	similarity or relevance of existing objects (e.g., documents,
	functions, commands, etc.) to a statement of our current needs (e.g.,
	the query). Towards this end, a variety of similarity measures have
	been proposed. However, the relationship between a measure's formula
	and its performance is not always obvious. A geometric analysis is
	advanced and its utility demonstrated through its application to six
	conventional information retrieval similarity measures and a seventh
	spreading activation measure. All seven similarity measures work with a
	representational scheme wherein a query and the database objects are
	represented as vectors of term weights. A geometric analysis
	characterizes each similarity measure by the nature of its
	iso-similarity contours in an n-space containing query and object
	vectors. This analysis reveals important differences among the
	similarity measures and suggests conditions in which these differences
	will affect retrieval performance. The cosine coefficient, for example,
	is shown to be insensitive to between-document differences in the
	magnitude of term weights while the inner product measure is sometimes
	overly affected by such differences. The context-sensitive spreading
	activation measure may overcome both of these limitations and deserves
	further study. The geometric analysis is intended to complement, and
	perhaps to guide, the empirical analysis of similarity measures.},
}