document: Applying collaborative filtering techniques to movie search for better ranking and browsing We propose a new ranking method , which combines recommender systems with information search tools for better search and browsing . Our method uses a collaborative filtering algorithm to generate personal item authorities for each user and combines them with item proximities for better ranking . To demonstrate our approach , we build a prototype movie search and browsing engine called MAD6 ( Movies , Actors and Directors ; 6 degrees of separation ) . We conduct offline and online tests of our ranking algorithm . For offline testing , we use Yahoo ! Search queries that resulted in a click on a Yahoo ! Movies or Internet Movie Database ( IMDB ) movie URL . Our online test involved 44 Yahoo ! employees providing subjective assessments of results quality . In both tests , our ranking methods show significantly better recall and quality than IMDB search and Yahoo ! Movies current search .
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