Cross-Category Recommendation Ranking with LLM Theme Mapping
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Solution Overview
Problem
Existing cloud-based service platforms face challenges in accurately and efficiently recommending additional data items from different categories due to noisy and limited user engagement data, compromising the accuracy and engagement level of cross-category recommendations.
Innovation Solution
A system utilizing a large language model (LLM) and platform-specific databases to determine theme-based recommendations, applying type selection models to generate an ordered list of items from related categories based on user interactions, enhancing relevance and engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If individual user engagement data is used for cross-category recommendations, then personalization is improved, but accuracy deteriorates due to noisy and limited data
Solution Approach 1:
The patent combines individual user engagement data with population-level engagement data to generate cross-category recommendations. This merging of data sources allows the system to leverage both personalization from individual user behavior and accuracy from aggregate population patterns, resolving the contradiction between personalized adaptation and measurement precision.
2Measurement precision
If population-level information is used to provide context, then accuracy is improved, but user-specific engagement level deteriorates
Solution Approach 1:
The patent applies local quality by differentiating the role of population-level data versus individual user data in the recommendation process. Population-level engagement data provides accurate contextual foundations for recommendations, while individual user engagement data enhances personalization and engagement levels. This localized application of different data qualities resolves the contradiction between accuracy and engagement.
3Adaptability or versatility
If millions of data items from numerous categories are available for selection, then versatility is improved, but recommendation accuracy deteriorates
Solution Approach 1:
The patent introduces engagement data (both individual and population-level) as an intermediary mechanism to bridge the gap between vast category coverage and recommendation accuracy. This intermediary data serves as a filtering and ranking mechanism that enables the system to accurately navigate and select from millions of data items across numerous categories, resolving the contradiction between versatility and precision.
Data Source
AI summary
This application is directed to systems and methods for cross-category item recommendation or ranking. In some embodiments, a disclosed method includes receiving interaction data indicative of an interaction with an information item associated with an anchor item in a first category; in accordance with a determination that the first category is associated with a plurality of themes of a second category, applying at least one type selection model to determine a set of item types associated with the plurality of themes of the second category; generating an ordered list of recommended items of the second category based on the set of item types; and in response to the interaction data, enabling display of the ordered list of recommended items of the second category on a display of a client device. In some embodiments, a large language model is applied to determine the plurality of themes of the second category.