Smart Cart Route Prediction for Timely Item Recommendations
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Solution Overview
Problem
Smart carts face challenges with irrelevant and untimely item recommendations due to redundant presentation of items already obtained or located in previously traversed regions, leading to inefficiencies in user interface relevancy and timing.
Innovation Solution
Implementing a route prediction algorithm in smart carts that uses sensors to track location and obtained items, applying a route prediction model to determine presentation constraints, ensuring items are highlighted before the user arrives at their location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If item recommendations are presented continuously without timing constraints, then the user interface provides constant information, but the recommendations become redundant and irrelevant when items are already obtained or located in previously traversed regions
Solution Approach 1:
The system performs preliminary actions by predicting the user's future route and pre-determining presentation constraints for recommendations before the user actually reaches the relevant store regions. This allows the system to present recommendations at the optimal time (before the user passes the item location) rather than continuously, eliminating redundancy while maintaining reliability.
Solution Approach 2:
The recommendation presentation system transitions from a static continuous display approach to a dynamic time-constrained approach. Presentation constraints are dynamically adjusted based on predicted user movement and location, allowing the system to adapt when and what to present, thereby reducing information redundancy while maintaining recommendation relevancy.
2Adaptability or versatility
If recommendations are presented without considering user route, then the system can present all candidate items, but recommendations become untimely by presenting items in previously traversed regions or out-of-the-way regions
Solution Approach 1:
The system predicts the user's future route in advance and uses this prediction to determine optimal presentation timing for each recommendation. By performing the routing analysis preliminarily, the system can filter out items in previously traversed regions and schedule presentations for future locations, maintaining comprehensive coverage while eliminating timing inefficiencies.
Solution Approach 2:
The system continuously monitors user location and compares it against the predicted route, using this feedback to adjust recommendation presentation timing. This closed-loop approach ensures recommendations are presented at the right moment along the user's actual path, improving time efficiency while maintaining adaptability to different user behaviors.
Data Source
AI summary
A smart cart presents candidate content objects to a user according to presentation constraints determined based on a predicted route of the smart cart. The smart cart obtains, from an item database, a plurality of candidate content objects to be presented to a user of a smart cart. The smart cart obtains a location of the smart cart in an environment. The smart cart applies a machine-learning route prediction model to the location of the smart cart to determine a future route of the smart cart. The smart cart determines, for each candidate content object, one or more presentation constraints based on the future route of the smart cart, wherein the presentation constraints constrain presentation of the candidate content object to the user to maximize a likelihood of the user engaging with the content object. The smart cart presents, via an electronic display, one or more of the candidate content objects according to the presentation constraints.