Cascading Category Recommender With User-Specific Category Embeddings
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Solution Overview
Problem
Existing category-level recommendation systems face challenges in providing strong negative samples, differentiating users with similar category histories, and maintaining high precision in recommending a concise set of categories, particularly for users with limited interaction history.
Innovation Solution
A cascading category recommender system comprising a candidate category model, a per-user category encoder, and a category prediction model, which uses item-level dependent category embeddings and a precise-centric loss function to generate user-specific category embeddings and select categories for recommendation, addressing the challenges of negative sample selection, user differentiation, and precision in category recommendation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional recommendation systems focus on item-level recommendations, then item-level precision can be improved, but the system becomes less effective for users with little-known information and past interactions (cold-start problem)
Solution Approach 1:
The recommendation system is segmented into two hierarchical levels: category-level recommendation and item-level recommendation. The category-level recommender handles cold-start users by first recommending categories, while the item-level recommender refines recommendations for users with sufficient interaction history. This segmentation allows each level to optimize for its specific requirements.
Solution Approach 2:
The system transitions from direct item-level recommendations to a two-dimensional approach: first recommending categories (coarse-grained), then recommending items within those categories (fine-grained). This dimensional change enables the system to handle users with limited interaction history by operating at the category level while still providing item-level precision when possible.
2Productivity
If category-level recommendation is used to expand user interests, then user engagement is improved, but the system becomes less precise in recommending specific items
Solution Approach 1:
The recommendation process is divided into two stages: category-level recommendation for exploration and item-level recommendation for precision. The category-level stage expands user interests by suggesting broad categories, while the item-level stage provides precise recommendations within those categories, thus resolving the precision-engagement trade-off.
Solution Approach 2:
The system dynamically switches between category-level and item-level recommendation based on user interaction history. For users with limited history, category-level recommendations are prioritized to expand engagement. As users interact with more items, the system gradually transitions to item-level recommendations to maintain precision, creating a dynamic adaptation to user needs.
3Measurement precision
If the system recommends a concise set of categories, then recommendation precision is improved, but the system becomes more difficult to differentiate users with similar category histories
Solution Approach 1:
The system applies local quality by generating user-specific category embeddings that capture nuanced user preferences within categories. Instead of treating all users with similar category histories uniformly, the system creates personalized embeddings that reflect individual user patterns, enabling precise differentiation even among users with similar broad category interests.
Solution Approach 2:
The system introduces category embeddings as an intermediary representation between raw category data and user preferences. These embeddings serve as a compressed, informative representation that captures both category information and user-specific patterns, allowing the system to differentiate users with similar category histories by examining their embedding characteristics rather than relying solely on surface-level category overlap.
Data Source
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AI summary
Some aspects relate to technologies for category recommendation for a listing platform using a cascading category recommender. The cascading category recommender includes a candidate category model, a per-user category encoder, and a category prediction model. Given a sequence of interacted categories for a user, the candidate category model selects candidate categories from a category set that sets forth categories for item listings on a listing platform. The per-user category encoder generates a category embedding for each interacted category based on interacted items for the user corresponding to each interacted category. The category prediction model selects categories for recommendation using the candidate categories, the sequence of interacted categories, and the category embeddings.