Collaborative Filtering for Web Marketing Efforts

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Recommender systems can improve consumer response to ecommerce sites. Large commercial sites make extraordinary resource demands on systems and databases. This paper describes a case study involving Columbia House, a large consumer direct marketing firm. Columbia House required rapid response time with high traffic, good recommendations at site-opening, and the ability to recommend new titles as they became available. LikeMinds developed a parallel collaborative filtering recommender that provided nearly linear speedup, making recommendations in less than 30 milliseconds on a single processor. A technique called "composite archetypes" helped seed the database with information from legacy transaction databases, making good recommendations at the time of the site opening. Another technique called "objective archetypes" allowed new titles to be recommended using only categorical information.