Recommendation Content Ranking for Cold-Start Unique Items
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Solution Overview
Problem
Existing recommendation systems struggle with the 'cold start' problem, where they are not well-suited to draw inferences for users or items about which they have not yet gathered sufficient information, particularly when dealing with unique or non-duplicable digital items.
Innovation Solution
A recommendation system that classifies digital items into two types based on event frequency ratios, using a generative model to predict unique items and rank potentially recommendable content based on the likelihood of user interaction, thereby addressing the cold start problem.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a recommendation system uses traditional filtering methods, then it can handle well-known items with sufficient data, but it fails to make accurate inferences for unique or non-duplicable items with limited interaction data
Solution Approach 1:
The patent introduces a new dimension to the recommendation problem by classifying items into frequent and rare categories based on event frequency ratios. This dimensional classification allows the system to apply different modeling strategies to different item types, thereby improving prediction accuracy for rare unique items without being constrained by the quantity of interaction data available.
Solution Approach 2:
The system changes the parameter of event frequency ratio to distinguish between frequent and rare items. By using this parameter, the system can adapt its recommendation approach based on the rarity of items, enabling accurate predictions for unique items with limited data through specialized handling of rare event patterns.
2Ease of manufacture
If the system treats all items uniformly, then the architecture remains simple, but it cannot optimize recommendations for unique items with limited interaction history
Solution Approach 1:
The patent segments items into two distinct categories: frequent items and rare items, based on their event frequency ratios. This segmentation allows the system to apply different processing strategies to each segment, improving recommendation reliability for rare unique items while maintaining architectural simplicity through a clear divide-and-conquer approach.
Solution Approach 2:
The system applies local quality by treating frequent and rare items differently according to their specific characteristics. Rare items receive specialized handling that accounts for their limited interaction data, while frequent items follow standard processing, thereby optimizing recommendation reliability for each item type without unnecessarily complicating the overall system.
3Productivity
If the system focuses on frequent items, then it can leverage abundant interaction data, but it cannot effectively recommend unique or rare items
Solution Approach 1:
The patent implements multi-functionality by designing a unified recommendation system that can handle both frequent and rare items through a single architecture. The system uses a common framework that incorporates specialized mechanisms for rare items, enabling it to maintain high productivity for frequent items while extending its versatility to effectively recommend unique and rare items as well.
Data Source
AI summary
A method and server for generating content for a user are disclosed. The method includes generating, via a first model, a first output for the user. The first output is representative of a predicted item of a first type. The method includes ranking, via a second model, the items of the second type into a ranked list based on the first output. A rank of an item is indicative of a likelihood of that the user interacts with the given item of the second type if the user performs an event of the pre-determined type on the given predicted item of the first type. Content is then selected from the ranked list and transmitted to a user device.