Real-Time Context Model Merging for New Recommendation Situations
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Solution Overview
Problem
Conventional methods fail to provide accurate and timely personalized recommendations when no existing context model exists for a user's situation, leading to potential revenue loss and reduced customer satisfaction.
Innovation Solution
A computer-implemented system and method that generates context models in real time by merging relevant models from similar situations, using contextual data from mobile devices to identify and provide contextually relevant recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a pre-established context model is used for a specific situation, then recommendation accuracy is improved, but the system cannot provide recommendations for new situations without time-consuming model building
Solution Approach 1:
The system pre-establishes multiple context models for different situations in advance. When a new situation arises, the system has already have pre-built models that can be quickly selected and merged, eliminating the need for time-consuming real-time model building while maintaining recommendation accuracy
Solution Approach 2:
The system creates a universal framework that can handle both existing and new situations. By implementing a model merging mechanism that combines multiple pre-established models, the system achieves multi-functionality - it can provide accurate recommendations for known situations using specific models and adapt to new situations by merging relevant models, thus covering all scenarios without sacrificing precision
2Measurement precision
If a new context model is built for a new situation, then recommendation personalization is improved, but time is lost during model building
Solution Approach 1:
The system performs model building in advance by pre-establishing multiple context models for various situations. When a new situation occurs, the system immediately merges relevant pre-built models rather than building from scratch, eliminating model building time while maintaining personalized recommendations through the merging process
Solution Approach 2:
The system merges multiple pre-established context models to create a personalized recommendation for new situations. By combining information from multiple existing models that are relevant to the new situation, the system achieves personalization without the time cost of building a completely new model, as the merging process rapidly synthesizes personalized recommendations from available data
3Device complexity
If population-based information is used when no specific model exists, then system simplicity is maintained, but recommendation relevance deteriorates
Solution Approach 1:
The system merges multiple pre-established context models that are relevant to the current situation, even when no exact match exists. This merging approach synthesizes situation-specific recommendations from multiple source models, providing relevant personalized recommendations without requiring a completely new complex model building process, thus maintaining system simplicity while improving recommendation relevance
Solution Approach 2:
The system dynamically selects and merges appropriate pre-established models based on the current situation characteristics. Rather than using a static population-based approach or requiring complex real-time model building, the system adaptively combines relevant existing models to generate situation-appropriate recommendations, balancing complexity and relevance through dynamic model selection and merging
Data Source
AI summary
A computer-implemented system and method for building context models in real time is provided. A contextual situation of a user is determined and compared with models each associated with actions that represent a situation. A determination is made that none of the models represents the situation of the user. A similarity value is determined for each of at least a portion of the models with the situation of the user. A threshold is applied to the similarity values. All models associated with a similarity value that satisfies the threshold are selected. The selected models are merged into a new model for the situation by utilizing a weight associated with each of the selected models to identify those actions in the selected models for inclusion in the new model.