Affinity Model for Mobile Content Personalization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Mobile devices do not effectively utilize historical user interaction data to provide personalized content, limiting user experience by not accounting for preferences and habits derived from past interactions at specific locations and times.
Innovation Solution
A method that generates an affinity model associating application types with locations and times of user interactions, allowing for personalized content delivery when the device is at corresponding locations and times, using parsing of application descriptions and contextual information to identify user patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If mobile devices provide general content without analyzing historical interactions, then device complexity is reduced, but user experience and content relevance deteriorate
Solution Approach 1:
The system performs preliminary analysis of user interactions with applications at specific locations and times, building an affinity model in advance that associates application types with contextual information. This pre-processing enables the device to automatically provide relevant content without requiring complex real-time analysis, thus achieving personalization while managing system complexity.
2Loss of information
If mobile devices analyze historical user interactions to provide personalized content, then content relevance improves, but processing time and computational resources increase
Solution Approach 1:
The system analyzes user interaction patterns and builds affinity models in advance, storing associations between application types, locations, and times. When the device is at a particular location and time, the pre-built model enables rapid retrieval of relevant content without requiring complex real-time analysis, thus minimizing processing time while preserving user preference information.
3Ease of operation
If mobile devices use simple content delivery without historical context, then ease of operation is maintained, but user engagement and satisfaction decrease
Solution Approach 1:
The system automatically analyzes user interactions with applications, determines application types by parsing descriptions, identifies locations using GPS data, and generates affinity models without requiring manual user input. The device self-optimizes content delivery by comparing current location and time against the affinity model, providing relevant content automatically while maintaining operational simplicity for the user.
Data Source
AI summary
Methods and computer products can provide personalized content based on historical interaction with a mobile device. A computing device can receive information about a user interaction with an application running on the mobile device at a first time and location. A type of the application can be identified by parsing a description of the application (e.g., using a natural language processing algorithm). An affinity model can be generated that associates the type of the application with the first time and/or location. At a second time and location, it can be determined that the second time corresponds to the first time and/or that the second location corresponds to the first location. Using the affinity model, the second time and/or location can be associated with the type of the application, and the mobile device may then display content related to the type of the application.