Use of secondary sources for location and behavior tracking
In a method modifying a user's virtual environment, a tracking module identifies a user identity and determines the location of the user. The tracking module retrieves historical user data associated with the user identity and the user location and identifies one or more patterns of user behavior using an analytical technique based on the historical user data. Upon determining one or more patterns from the data, the tracking module predicts user behavior associated with at least one parameter associated with the location and modifies at least one of a user virtual environment and a user avatar characteristics based on the predicted user behavior.
1. A method of using one or more computer processors to modify a virtual environment about a user, comprising:
determining a user identity;
determining a location of the user;
retrieving historical user data associated with the user identity and the user location;
identifying one or more patterns of user behavior using an analytical technique based on the historical user data;
predicting a user behavior associated with at least one parameter associated with the location;
modifying at least one of a user virtual environment and a user avatar characteristic based on the predicted user behavior, wherein modifying the user virtual environment comprises modifying avatar functionality in the user virtual environment and wherein the avatar functionality is selected from the group consisting of: social media functions, commerce-based functions, environment customization tools, and access to a restricted virtual space;
determining one or more suggested user actions to the user based on the predicted user behavior and a social network of the user; and
sending the one or more suggested user actions to the user.
2. The method of claim 1 , wherein the analytical technique comprises a traditional algorithm.
3. The method of claim 1 , wherein the analytical technique comprises a machine learning algorithm.
4. The method of claim 3 , wherein the machine learning algorithm is selected from a group consisting of: a linear regression algorithm, a supervised learning classifier, and a time-series analysis.
5. The method of claim 1 , wherein the user virtual environment comprises a shared virtual environment comprising multiple users.
6. The method of claim 1 , wherein the user avatar characteristics comprise at least one of an avatar appearance and an avatar action.
7. The method of claim 1 , further comprising storing the user identity and the location of the user in the historical user data.
8. The method of claim 1 , wherein the historical user data is limited to user data approved for collection by the user.
9. The method of claim 1 , wherein the historical user data is compiled from third-party data sources.