Location-Based Preference Attribution for Content Targeting
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Solution Overview
Problem
Current content presentation methods on the Internet fail to effectively utilize user preferences attributed to locations, leading to inefficiencies in targeting relevant content to users based on their geographical context and preferences.
Innovation Solution
A method that identifies social actions with preference designations, attributes these preferences to both the user and the location associated with the action, and uses this information to target content to users based on their location and preferences, allowing for the serving of relevant content items such as advertisements and popular web pages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If content is targeted based on user preferences, then content relevance is improved, but the system cannot effectively utilize location-based preference data
Solution Approach 1:
The patent extends the preference attribution from a single user dimension to a dual dimension by also attributing preferences to geographic locations. When a user performs a social action expressing preference, the system not only stores the user's preference but also associates it with the user's current location, creating location-based preference profiles that can be independently queried and utilized for targeted content delivery.
2Measurement precision
If preferences are attributed to locations, then content targeting accuracy is improved, but data storage and processing complexity increases
Solution Approach 1:
The patent combines user preference data with location data into a unified attribution system. Rather than maintaining separate user profiles and location databases, the system merges these dimensions by associating preferences with both user identifiers and location identifiers, allowing the same preference data to serve multiple targeting purposes simultaneously.
3Measurement precision
If social actions are used to identify preferences, then preference accuracy is improved, but the system complexity for tracking and processing social actions increases
Solution Approach 1:
The system leverages existing social graph data and user-generated content interactions as natural preference indicators. Rather than requiring users to explicitly state their preferences, the system automatically interprets social actions (such as sharing, liking, or commenting on content) as implicit preference expressions, reducing the need for complex preference elicitation mechanisms.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer-readable storage medium, for providing content, comprising: identifying a social action that includes a preference designation for an object; determining a location of an individual user associated with the social action or a location associated with the object that is the subject of the preference designation; attributing the preference designation to both the location and to the individual user, where the attributed preference designation can be used to target further content to either the individual user or other users; and receiving a request for content that is related to the location and providing, responsive to the request, one or more content items based on the attributed preference designations.