Recommender system and method for generating implicit ratings based on user interactions with handheld devices

This recommends items based on how a user interacts with a handheld device.  Users are given a repetitive task to do on a mobile device.  Their interactions are recorded in a history.  This history is used to create recommendations.  Unlike Amazon's content based system, this system takes into account how recent an event or interaction occurred.  More recent interactions are considered to be more relevant.

According to the claims, the calculations for the recommendations are made in the following three ways:

• "rating(item)=number of interactions(item) since datetime(item acquired)/number of total interactions (item) since datetime(item acquired)."
• "rating(item)=total interaction time(item)/size(item)"
  

• rating(item)=[total interaction time(item)/size(item)*exp(−damping coefficient]*(date−time acquired).

The first method takes into account the recency of the interaction.  The second method takes into account the amount of time that the user spent on a particular interaction.  The third method takes into account something that I don't understand.  I can't figure out what the difference is between claim 2 and claim 3.  They appear to be the same with the exception of the calculations.

  Mobile devices that the patent covers in its data gathering include cell phones, mp3 players, PDAs, and electronic book readers.

**WARNING this was probably written by a lawyer***

http://www.freepatentsonline.com/6947922.html

http://www.google.com/patents?id=QkEWAAAAEBAJ&dq=09/596070