Collaborative Filtering for Web Marketing Efforts

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. Introduction Collaborative filtering is a real-time personalization technique. It leverages similarities between people to make recommendations, and can be more accurate than mechanical techniques, such as rule-based systems or Bayesian methods. LikeMinds uses collaborative filtering technology to improve marketing efforts for direct marketers: accurate personalization tends to drive up revenues on commercial web sites. LikeMinds developer John Hey created the first general collaborative filtering system in 1987. It was originally deployed in video recommender kiosks around Boston. Since that time, web commercialization has placed increasing demands on the technique. Commercial Needs Columbia House was one of the first large consumer retailers to move to the web. Columbia House markets music, videos and CDROMs directly to consumers as a loyalty club, originally through a direct mail marketing channel. Music customers receive 12 titles free for signing up, promising to purchase 6 more titles at regular price. Columbia House has historically used rule-based personalization in its business, hiring a small army of analysts to assign each new album to one or more listening preferences. The first Columbia House web site used this personalization method. However, with broad categories, such as "alternative rock," rule-based personalization was not very personal. Columbia House decided to create a new site that would make more individualized recommendations to visitors. It chose LikeMinds collaborative filtering technology for this effort. The Columbia House site required real-time performance under heavy usage. Columbia House has the second highest number of ecommerce transactions of all web sites, and offers thousands of products. It had an existing relational database that provided customer and product information, and wanted to store ratings and intermediate values in its database. For the first time, a major consumer products company wanted to integrate collaborative filtering into every aspect of a site, making performance a critical issue. Whenever a customer searched for albums, the site would state which albums were recommended. Nearly every page view demanded personalization. The recommended albums were to be different for each customer: segmentation, which increases speed at the expense of accuracy, could not be used to speed the process. From: AAAI Technical Report WS-98-08. Compilation copyright © 1998, AAAI (www.aaai.org). All rights reserved.