Recommendation System Entity Segmentation for Adaptability
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Solution Overview
Problem
Conventional recommendation systems are slow to adjust when an entity's interactions with items differ from those of other entities, leading to many irrelevant recommendations and inefficient use of computational and network resources.
Innovation Solution
A recommendation system segments entities into sets based on prior interaction data, scales interactions, and generates a subset of entities with the highest interactions to form a recommendation model, using this model to provide relevant recommendations more efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional recommendation systems use prior interaction data from all entities to generate recommendations, then the system can provide personalized recommendations, but the system is slow to adjust when an entity's interactions differ from others and generates many irrelevant recommendations
Solution Approach 1:
The patent segments entities into different groups based on their interaction characteristics with items. By dividing the entity population into segments with similar interaction patterns, the system can provide tailored recommendations for each segment, improving adaptability while reducing the time needed to learn relevant recommendations for individual entities.
2Reliability
If conventional systems process all prior interaction data to generate recommendations, then comprehensive recommendations can be provided, but computational resources and network resources are inefficiently used
Solution Approach 1:
The patent extracts and utilizes only the necessary subset of prior interaction data needed to generate relevant recommendations. By identifying and processing only the critical interaction patterns rather than all available data, the system maintains recommendation reliability while significantly improving computational and network resource efficiency.
3Adaptability or versatility
If Bayesian approaches update prior probability distribution through multiple iterations, then the system can learn entity preferences, but the entity receives many irrelevant recommendations during the iteration process
Solution Approach 1:
The patent performs preliminary analysis of interaction data to identify key patterns and segments before generating recommendations. By preparing and organizing the data structure in advance, the system can quickly adapt to entity preferences with fewer iterations, reducing the number of irrelevant recommendations provided during the learning process.
Data Source
AI summary
In implementations of systems for generating recommendations, a computing device implements a recommendation system to receive prior interaction data describing prior interactions of entities with items. The recommendation system processes the prior interaction data and segments the entities into a first set and a second set. The entities included in the first set have greater numbers of prior interactions with the items than the entities included in the second set. The recommendation system then generates subset data describing a subset of the entities in the first set. This subset excludes entities having numbers of the prior interactions with the items below a threshold. The recommendation system forms a recommendation model based on the subset data and the system uses the recommendation model to generate a recommendation for display in a user interface.