Context-Aware Application Similarity via Intent Distribution
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Solution Overview
Problem
Current recommendation services for applications fail to account for context-dependent user preferences, as they rely solely on content similarities and do not recognize changes in user behavior based on different contextual environments.
Innovation Solution
A system that determines context-aware similarity between applications by processing context logs to identify latent context intents and probabilistic distributions, allowing for context-based recommendations that adapt to changing user preferences across various contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If recommendation services rely solely on content similarities, then the implementation is simple, but the recommendations fail to account for context-dependent user preferences
Solution Approach 1:
The patent segments the recommendation system into multiple independent modules: a context log processing module that analyzes user behavior data, a distribution determination module that calculates application usage patterns, and a similarity determination module that computes context-aware similarity scores. This segmentation allows the system to incorporate complex context analysis while maintaining manageable system architecture and enabling incremental implementation.
Solution Approach 2:
The patent introduces context logs as an intermediary data structure that mediates between raw user behavior data and the recommendation engine. These context logs capture contextual information (time, location, device state) and serve as the foundation for determining application distributions, thereby bridging the gap between simple content-based filtering and complex context-aware recommendations without requiring direct complex interactions between all system components.
2Measurement precision
If the system processes context logs to determine context intents and distributions, then the recommendation accuracy improves, but the computational time and resources increase
Solution Approach 1:
The patent performs preliminary processing of context logs to determine context intents and application distributions in advance, before actual recommendation requests are made. By pre-analyzing user behavior patterns and storing these contextual distributions, the system avoids performing complex computations in real-time when recommendations are needed, thereby reducing latency while maintaining high accuracy.
Solution Approach 2:
The system implements feedback mechanisms where context logs continuously capture user interactions with recommended applications, and this feedback is used to refine and update application distributions. This iterative feedback loop allows the system to improve recommendation accuracy over time while progressively optimizing processing efficiency based on learned patterns from accumulated data.
Data Source
AI summary
An approach is provided for determining the context-aware similarity between applications. A similarity platform processes one or more context logs associated with one or more devices to determine one or more context intents. The similarity platform further determines one or more distributions of one or more applications executed at the one or more devices with respect to the one or more context intents. The similarity platform also determines a context-aware similarity between two or more applications based, at least in part, on the one or more application distributions.