Push-Based Recommendation System for Proactive User Engagement
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Solution Overview
Problem
Current systems fail to provide users with unsolicited recommendations based on their observed behaviors and real-world changes, limiting the ability to proactively suggest relevant entities or actions without explicit user solicitation.
Innovation Solution
A system that identifies triggering events associated with user behavior, constructs an entity selection criteria model using various constraints, and applies it to suggest entities, ranking them for relevance and convenience, allowing for unsolicited recommendations to be pushed to the user's device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the system provides unsolicited recommendations based on observed user behavior, then user experience is improved through proactive suggestions, but system complexity increases due to the need for behavior monitoring and model construction
Solution Approach 1:
The system performs preliminary actions by monitoring user behavior and constructing entity selection criteria models in advance, before users explicitly request recommendations. This allows the system to proactively push relevant suggestions when triggering events occur, improving user experience without requiring users to initiate search queries.
Solution Approach 2:
The system enables self-service by automatically monitoring user behavior, constructing selection criteria models, and generating recommendations without requiring user intervention. The system serves itself by autonomously identifying patterns in user actions and pushing contextualized suggestions, reducing the need for users to manually search for information.
2Measurement precision
If the system monitors user behavior and constructs models in real-time, then recommendation relevance is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary behavior monitoring and model construction in advance, building entity selection criteria models before triggering events occur. This preliminary action allows the system to have pre-computed models ready for rapid recommendation generation when events are detected, improving recommendation relevance while reducing real-time processing delays.
Solution Approach 2:
The system employs dynamic model construction that adapts to user behavior patterns over time. The entity selection criteria models are constructed and updated based on observed behaviors, allowing the system to improve recommendation relevance dynamically while optimizing processing efficiency through learned patterns rather than continuous real-time analysis.
Data Source
AI summary
Among other things, one or more techniques and/or systems are provided for pushing a recommendation to a user. That is, a recommendation may be pushed to a device of the user based upon a triggering event associated with the user. The recommendation may be provided, for example, without user solicitation for the recommendation. In one example, a recommendation component may observe that the user frequently stops for ice cream on Fridays after work (e.g., based upon prior social network check-ins). Accordingly, on the following Friday, the recommendation component may push a recommendation to the user's device to visit a particular grocery store on the way home from work that is within 10 minutes of the user's home so that the user can avoid melting ice cream (e.g., a location constraint may be applied to choose a grocery store that is relatively close to the user's home).