Applying collaborative filtering techniques to movie search for better ranking and browsing

Abstract

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. © 2007 ACM.