Embedding-Space Item Ranking for Real-Time Personalization
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Solution Overview
Problem
Existing recommendation systems face challenges in providing accurate, real-time personalized recommendations due to high computational complexity and resource inefficiency, especially in spaces with unique and dynamic inventory items.
Innovation Solution
A two-part recommendation system comprising a candidate generator that generates a non-personalized candidate item list offline and a candidate ranker that personalizes the list using a distance function in an embedding space based on user-specific centroids, reducing computational load and enabling real-time personalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex machine learning techniques are used for personalized recommendations, then recommendation accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The recommendation system is divided into two independent modules: an offline candidate generator that creates item lists based on item features, and an online ranker that applies personalized machine learning models. This segmentation allows complex ML operations to be performed only on pre-filtered candidate sets rather than entire item catalogs, reducing real-time computational complexity while maintaining recommendation accuracy.
Solution Approach 2:
The candidate generator performs preliminary filtering and item list creation offline before the user makes a selection. By pre-processing and organizing items into candidate lists based on item features and relationships, the system reduces the scope of items that require complex personalized ranking, thereby lowering online computational complexity while preserving accuracy.
2Measurement precision
If complex machine learning techniques are used for personalized recommendations, then recommendation accuracy is improved, but resource efficiency deteriorates
Solution Approach 1:
By separating the recommendation system into offline candidate generation and online personalized ranking, the system avoids applying resource-intensive machine learning models to entire item catalogs. Instead, ML models are applied only to small candidate sets (e.g., 10-100 items), dramatically reducing computational resources and energy consumption while maintaining high recommendation accuracy.
Solution Approach 2:
The system applies personalized machine learning ranking only to a partial subset of items (the candidate list) rather than all available items. This partial action approach focuses computational resources on the most relevant items, improving resource efficiency while maintaining or enhancing recommendation accuracy through targeted personalized processing.
3Productivity
If real-time personalized recommendations are provided, then user engagement is enhanced, but processing time increases
Solution Approach 1:
The system separates time-consuming operations into offline and online components. The candidate generator creates item lists offline without real-time constraints, while the online ranker quickly applies personalized ranking to pre-generated candidate lists. This segmentation enables real-time personalized recommendations with low processing time, enhancing user engagement without sacrificing speed.
Solution Approach 2:
By pre-generating candidate item lists offline based on item features and relationships, the system prepares data structures that enable rapid online personalization. When a user selects an item, the system only needs to rank a small pre-filtered candidate set rather than processing the entire item catalog, achieving real-time performance that enhances user engagement.
Data Source
AI summary
Systems and methods for a two-part recommendation system wherein a non-personalized item-to-candidate item list is generated without personalization and the items within the corresponding candidate list may be ranked in order to personalize that list to the particular user. In an embodiment, ranking occurs based on a distance function between individual items in the list and the reference item, such as a distance between the items within an embedding space that represents relevant features of items as vectors in latent space. Accordingly, ranking by a candidate ranker can select which items in the candidate list are most pertinent and personalized to the user at a present time. Because ranking can require significantly fewer resources than generating the candidate list, this two-part system can enable real-time recommendations that are personalized to the user.