POI Recommendation Training for Long-Run Resource Allocation
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Solution Overview
Problem
Existing point-of-interest recommendation systems often provide local optimum solutions based on greedy methods, leading to suboptimal resource allocation and low long-run returns for multiple points of interest.
Innovation Solution
A training method for a point-of-interest recommendation model that adjusts initialization parameters based on the difference between current and target returns, considering environmental state parameters and predictive use information to optimize global recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If greedy methods are used for point-of-interest recommendation, then immediate recommendation speed is improved, but long-run resource allocation efficiency deteriorates
Solution Approach 1:
The system performs preliminary actions by determining environmental state parameters including predictive use information before making recommendations. This allows the model to anticipate future resource occupation and make recommendations that optimize long-run returns rather than just immediate speed, resolving the contradiction between fast recommendations and efficient resource allocation.
Solution Approach 2:
The training method uses feedback from the difference between current return and target return to continuously adjust model parameters. The target return is determined based on future recommendation requests, creating a feedback loop that improves resource allocation efficiency while maintaining recommendation capability, thus resolving the contradiction between speed and productivity.
2Ease of manufacture
If traditional recommendation methods are used, then implementation simplicity is improved, but recommendation accuracy deteriorates
Solution Approach 1:
The system changes parameters by determining environmental state parameters that include resource occupation information and predictive use information. These parameter changes enable the model to achieve higher recommendation accuracy by considering future states, while the automated training process based on return differences maintains implementation feasibility, resolving the contradiction between simplicity and accuracy.
Data Source
AI summary
A training method for a point-of-interest recommendation model and a method for recommending a point of interest are provided. An implementation solution includes: obtaining training data including a plurality of point-of-interest recommendation requests; determining initialization parameters of the point-of-interest recommendation model; for a first point-of-interest recommendation request among the plurality of point-of-interest recommendation requests, determining a current return for the first point-of-interest recommendation request by utilizing the point-of-interest recommendation model, and determining, based on a second point-of-interest recommendation request initiated after the first point-of-interest recommendation request is completed, a target return for the first point-of-interest recommendation request by utilizing the point-of-interest recommendation model; and adjusting the initialization parameters of the point-of-interest recommendation model based on a difference between the current return and the target return for the first point-of-interest recommendation request, to obtain final parameters of the point-of-interest recommendation model.