A
system and method is disclosed for recommending items to individual users using a combination of clustering decision trees and frequency-based term mapping. The
system and method of the present invention is configured to receive data based on user action, such as television
remote control activity, or
computer keyboard entry, and when a new item data is made available from sources such as television program guides, movie databases, deliverers of advertising data, on-line auction web sites, and
electronic mail servers, the
system and method analytically breaks down the new item data, compares it to ascertained attributes of item data that a user liked in the past, and produces numeric
ranking of the new item data dynamically, and without subsequent
user input, or data manipulation by item data deliverers, and is tailored to each individual user. A embodiment is disclosed for learning user interests based on user actions and then applying the learned knowledge to rank, recommend, and / or filter items, such as e-mail spam, based on the level of interest to a user. The embodiment may be used for automated personalized information learning, recommendation, and / or filtering systems in applications such as
television programming, web-based auctions,
targeted advertising, and
electronic mail filtering. The embodiment may be structured to generate item descriptions, learn items of interest, learn terms that effectively describe the items, cluster similar items in a compact
data structure, and then use the structure to rank new offerings. Embodiments of the present invention include, by way of non-limiting example: allowing the assignment of rank scores to candidate items so one can be recommended over another, building decision trees incrementally using
unsupervised learning to cluster examples into categories automatically, consistency with “edge” (thick
client) computing whereby certain data structures and most of the
processing are localized to the set-top box or local PC, the ability to learn content attributes automatically on-the-fly, and the ability to store user preferences in opaque local data structures and are not easily traceable to individual users.