Hypergraph Collaborative Filtering for Recommendation Accuracy
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Solution Overview
Problem
Conventional recommendation systems fail to effectively capture higher-order relationships between users and items, leading to sub-optimal and inaccurate recommendations due to their reliance on bipartite graphs that ignore intra-domain similarities and suffer from sparse data distributions.
Innovation Solution
The implementation of a hypergraph-based collaborative filtering framework that applies a semantic clustering model to determine user and item embeddings, constructs a hypergraph to explore higher-order relations, and uses contrastive learning to balance collaborative and higher-order views, thereby improving recommendation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional recommendation systems use bipartite graphs to model user-item interactions, then the system structure is simple and easy to implement, but the system cannot capture higher-order relationships and intra-domain similarities, leading to inaccurate recommendations
Solution Approach 1:
The patent transitions from traditional bipartite graphs (2-dimensional user-item relationships) to hypergraphs that incorporate higher-order relationships and intra-domain similarities. This dimensional expansion allows the system to capture complex patterns such as user-user similarities, item-item similarities, and group-based relationships, thereby improving recommendation accuracy without being constrained by simple structural assumptions
2Measurement precision
If conventional recommendation systems rely on sparse user-item interaction data, then the data collection process is simple, but the recommendations become sub-optimal due to insufficient information
Solution Approach 1:
The patent merges multiple data sources and relationship types into a unified hypergraph framework. It combines user-item interactions with user-user similarities, item-item similarities, and higher-order group relationships, effectively densifying the data representation. This integration transforms sparse individual interaction data into rich multi-relational structures that provide sufficient information for accurate recommendations
Solution Approach 2:
The hypergraph structure acts as an intermediary that connects and integrates disparate data elements. It mediates between sparse user-item interactions and the need for dense relational information by introducing hyperedges that represent higher-order relationships, thereby bridging the gap between limited direct interactions and comprehensive recommendation requirements
Data Source
AI summary
An electronic device and a method for implementation of hypergraph-based collaborative filtering recommendations. The electronic device receives a collaborative filtering graph corresponding to a set of users and a set of items. The electronic device determines a first set of user embeddings and a first set of item embeddings. The electronic device applies a semantic clustering model to determine a second set of user embeddings and a second set of item embeddings. The electronic device constructs a hypergraph to determine a third set of user embeddings and a third set of item embeddings. The electronic device determines a first contrastive loss and a second contrastive loss to determine a collaborative filtering score. The electronic device determines a recommendation of an item for a user based on the determined collaborative filtering score. The electronic device renders the determined recommended item on a display device.