Mobile Device Context Inference for App Recommendations
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Solution Overview
Problem
The proliferation of mobile applications in app stores makes it difficult for users to find relevant apps without extensive searching, especially for mobile users who do not frequently engage in search requests, as existing systems lack the ability to infer user motivations and provide tailored recommendations.
Innovation Solution
A system that creates situational profiles based on sensory data from mobile devices, using an environment map and rules database to recommend apps, coupons, or advertisements, by inferring the user's situation and environment, and dynamically assigning profiles to manage device actions and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually search for applications in app stores, then they can find relevant apps, but the process becomes time-consuming and complex especially with hundreds of thousands of apps available
Solution Approach 1:
The system performs preliminary actions by continuously monitoring sensory data from the mobile device (location, orientation, motion, ambient light, temperature) and pre-processing this information to infer user situation and context. This allows the system to have app recommendations ready before the user actively searches, eliminating the need for time-consuming manual searches while maintaining high relevance matching.
Solution Approach 2:
The system enables self-service by automatically inferring user context from device sensors and autonomously generating app recommendations without requiring user input or active search requests. The system serves itself by monitoring its own environment and making intelligent decisions about what apps to recommend based on the inferred situational profile.
2Adaptability or versatility
If the system continuously monitors sensory data to infer user situation, then personalized recommendations can be provided, but device complexity and energy consumption increase
Solution Approach 1:
The system applies universality by using a single integrated framework that processes multiple types of sensory data (location, orientation, motion, ambient light, temperature) through a unified situational profile generation mechanism. This multi-functional approach allows the same system components to handle various sensor inputs and generate comprehensive context awareness without requiring separate complex processing pipelines for each sensor type.
Solution Approach 2:
The system merges multiple sensor data streams and processing functions into a unified situational profile generation process. By combining location data, orientation data, motion data, ambient light data, and temperature data into a single contextual representation, the system reduces overall complexity while maintaining comprehensive context awareness.
3Productivity
If the system infers user motivation without search requests, then recommendations can be provided proactively, but accuracy of inference may decrease
Solution Approach 1:
The system implements feedback by continuously monitoring sensory data and comparing the inferred situational profile against the actual user context. The system uses this feedback loop to refine its inference accuracy over time, adjusting its understanding of user motivation based on patterns observed from multiple data points and user responses to recommendations.
Solution Approach 2:
The system performs preliminary inference actions by analyzing sensory data patterns before user actions occur. By pre-processing sensor data and establishing baseline patterns of user behavior in different situations, the system can accurately infer user motivation in advance, improving both the speed and accuracy of recommendations.
Data Source
AI summary
A system and method is provided for using information broadcast by devices and resources in the immediate vicinity of a mobile device, or by sensors located within the mobile device itself, to ascertain and make a determination of the immediate environment and state of the mobile device. The sensor data is then used to identify situational profiles to target and determine the relevance of apps, advertisements, content, and recommendations.