Multi-layer Recommendation System for Dynamic Marketplace Optimization
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Solution Overview
Problem
Dynamic marketplaces face challenges in simultaneously optimizing objectives for multiple categories of participants, such as restaurants, consumers, and delivery partners, due to competing objectives and rapidly changing dynamics.
Innovation Solution
A multi-layered recommendation system that applies objective models independently to predict values such as demand, market fairness, earnings, and happiness, and then uses optimization techniques like linear or quadratic programming to generate personalized recommendations that balance multiple objectives in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single optimization model is used to generate recommendations, then the system complexity is low, but it cannot simultaneously optimize multiple competing objectives for different marketplace participants
Solution Approach 1:
The patent divides the optimization system into multiple independent layers: a first layer with separate objective models for each marketplace participant category (restaurants, consumers, delivery partners), and a second layer that integrates these models. Each objective model independently optimizes for its specific objective without interfering with others, allowing the system to handle multiple competing objectives while maintaining manageable complexity through modular design.
2Adaptability or versatility
If objective models are trained independently in separate layers, then the system is flexible to add or remove objectives without retraining, but the integration of multiple objectives requires complex optimization algorithms
Solution Approach 1:
The system segments the optimization process into independent objective models in the first layer, each trained separately on its specific data. This segmentation enables flexible addition or removal of objectives by simply adding or removing individual models without affecting others. The second layer then integrates these segmented models through coordinated optimization, balancing the benefits of independence with the need for holistic optimization.
Solution Approach 2:
The second layer acts as an intermediary between the independent objective models in the first layer. It receives outputs from multiple objective models and coordinates their integration through optimization algorithms, mediating the conflicts between competing objectives and producing a final recommendation that balances all considerations without requiring direct interaction between the independent models.
3Reliability
If the system optimizes for all marketplace participants simultaneously, then marketplace fairness is improved, but real-time optimization becomes computationally intensive
Solution Approach 1:
The system performs preliminary actions by pre-training independent objective models for each marketplace participant category during periods when real-time optimization is not required. These models capture the objectives and preferences of restaurants, consumers, and delivery partners in advance. During real-time operation, the system only needs to integrate these pre-trained models through the second layer, significantly reducing computational requirements while maintaining fairness across all participants.
Data Source
AI summary
A computing system generates recommendations for users within the context of a network service. To account for objectives of various users associated with the network service, some of which may not reach optimality at the same time, the computing system generates values associated with each of the objectives separately. For example, for each objective, the system may train a computer model to produce a representative value. To generate a recommendation of an entity for a user, the system uses the generated objective values as inputs to an optimization algorithm. The optimization step may use linear programming or quadratic programming to generate a recommendation score, for example. This two-step process allows the system to account for multiple objectives and makes the system easily adaptable to change when the set of objectives is updated.