Resource-Constrained Sequential Recommendation System
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Solution Overview
Problem
Conventional recommender systems face challenges in accurately capturing user preferences and contextual history, leading to inefficient recommendations due to high sample complexity and resource constraints, which can result in user dissatisfaction and infrastructure overload.
Innovation Solution
A resource-constrained sequential recommender system determines user types and preferences by analyzing user data and resource constraints, using Thompson sampling and Mixed-Observability Markov Decision Processes to generate recommendations that account for user interests and system capacity, thereby avoiding overloading and improving user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional recommender systems use reinforcement learning to capture contextual history, then recommendation accuracy is improved, but computing resources and processing time increase significantly
Solution Approach 1:
The patent segments the user population into distinct user types based on behavioral patterns and preferences. Instead of applying complex reinforcement learning to each individual user, the system creates separate recommendation models for each user type segment, significantly reducing computational resources while maintaining contextual history capture capabilities.
Solution Approach 2:
The system applies reinforcement learning selectively - not to all users simultaneously, but to learn user type classifications and recommendation patterns for each segment. This partial application of the complex algorithm reduces overall computing resource requirements while still capturing contextual history where most beneficial.
2Productivity
If conventional recommender systems increase server capacity to handle more recommendations, then user service capability is improved, but infrastructure cost and resource consumption increase
Solution Approach 1:
By segmenting users into types and creating specialized recommendation models for each segment, the system improves service capability for each user group without needing to proportionally increase overall server capacity. Each segmented model is more efficient at serving its specific user type.
Solution Approach 2:
The system changes the parameter of recommendation generation by using user type-specific models rather than a single general model. This parameter change allows the same infrastructure to serve more users effectively by optimizing recommendations for each user segment's specific characteristics.
3Productivity
If conventional recommender systems make more recommendations to users, then user engagement is improved, but server overload and system failure risk increase
Solution Approach 1:
The system makes recommendations selectively based on user type and contextual history rather than uniformly to all users. This partial action approach maintains high engagement for relevant users while avoiding server overload from generating excessive irrelevant recommendations.
Solution Approach 2:
The reinforcement learning models incorporate feedback from user interactions to learn which recommendations are most effective for each user type. This feedback mechanism ensures recommendations are made only when likely to engage users, preventing server overload from futile recommendation attempts.
Data Source
AI summary
The present disclosure relates to recommending points of interest to a plurality of users based on a type of each user as well as constraints associated with the points of interest. For example, one or more embodiments determine a user type for each user and determine user preferences based on the user type. Additionally, the system can determine resource constraints associated with each point of interest, indicating limitations on the capacity of each associated resource. The system can then provide recommendations to the plurality of users based on the user types and the resource constraints. In particular, the system can recommend points of interest that satisfy the preferences corresponding to each user type subject to the resource constraints of each point of interest. For example, one or more embodiments involve solving a linear program that takes into account user types to obtain recommendation policies subject to the resource constraints.