Applying Collaborative Filtering Techniques to Movie Search for Better Ranking and Browsing

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In general web search engines, such as Google and Yahoo! Search, document relevance for the given query and item authority are two major components of the ranking system. However, many information search tools in ecommerce sites ignore item authority in their ranking systems. In part, this may stem from the relative difficulty of generating item authorities due to the different characteristics of documents (or items) between ecommerce sites and the web. Links between documents in an ecommerce site often represent relationship rather than recommendation. For example, two documents (items) are connected since both are produced by the same company. 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 engine called MAD6 (Movies, Actors and Directors; 6 degrees of separation).