Sharing Recommendation Agents

Abstract

A customized recommendation agent for the user is built using a behavioral model. The customized recommendation agent selects recommendations from a corpus to present to the user, based on the behavioral model and the user's current or predicted future context. The customized recommendation agent can be shared by the user with others, thus allowing others to access recommendations that may appeal to the user, for example, for use in planning joint activities. Because the user's recommendation agent is independent from the user's actual history, preferences can be shared without revealing a user's specific behavior.

Description

CROSS-REFERENCE TO RELATED APPLICATION[0001]This application claims the benefit of U.S. Provisional Application No. 61 / 675,733, filed Jul. 25, 2012, entitled “Recommendation Agents Using Routine, Personality, Learning, and Sharing,” and U.S. Provisional Application No. 61 / 675,732, filed Jul. 25, 2012, entitled “Creating a Storyline from Mobile Device Data,” both of which are incorporated herein by reference in their entirety.BACKGROUND[0002]1. Technical Field[0003]The described embodiments pertain to making recommendations to a user based on location data and other data collected from mobile devices.[0004]2. Description of Related Art[0005]A variety of personalized recommendation systems currently exist. These systems typically record user events (such as purchases of particular products) and / or preferences (such as ratings of products and media) to generate a model for the user. This model is then used to recommend “similar” items to the user. However, there are a number of obstacl...

AI Technical Summary

Benefits of technology

This patent describes a method, computer-readable storage medium, and system for creating a customized recommendation agent for a user based on their behavior and current or predicted future context. This agent can recommend things to the user that may interest them, and can be shared with others without revealing the user's specific behavior. This allows for privacy while sharing recommendations. The technical effects of this invention are improved recommendations for users based on their specific behavior and context, while still maintaining their privacy.

Problems solved by technology

However, there are a number of obstacles to building useful applications on top of the kinds of user models currently available.

First, existing models are not very effective until the system has observed a great deal of user behavior or preferences.

As a result, many recommendation systems are limited in their usefulness to particular scenarios.

However, they cannot help the pizza fan find a great burger joint, the science fiction fan find her friend's favorite science fiction novel, or the Jennifer Aniston fan find a bar that makes a great Martini.

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