We begin with two articles suggesting the possible separation of document and query vector space. Viewing information retrieval as a topology on a document space determined by a similarity function between queries and documents gives what Egghe and Rousseau call a retrieval topology. Such topologies might use a pseudo metric which measures the distance between documents independent of the query space, or might make all similarity functions between documents and queries continuous, called here the similarity topology. The topological model allows the introduction of Boolean operators. The inner product is suggested as producing a more powerful model than the cosine measure.
Independence remains desirable in document space.
It would appear that the assumptions that documents and queries are elements of the same space, and that term independence is required, are not warranted. Fares presents a new loan policy model which incorporates a decision variable for maximum books to be borrowed, along with the traditional loan period, and adds user satisfaction with policies to the usual book availability satisfaction indicator. Each indicator is defined as the ratio of satisfied demand to total demand. Number of renewals, duplications, demand, and reservations are considered to have a very small effect. On the Fusion of Documents from Multiple Collection Information Retrieval Systems Ronald R. Yager and Alexander Rybalov Yager and Rybalov assume m retrieval systems without file overlap each providing a ranked list of texts based upon their varying ranking criteria, and in response to a common query, and define fusion as the construction of a single ordered list of the n most relevant texts over all m system responses.