A Network Embeddings Based Recommendation Model with Multi-Factor Consideration

Explore a 26-minute conference talk from EuroPython 2022 that delves into a Network Embeddings based Recommendation Model with multi-factor consideration. Learn about a three-step method for improving recommendation accuracy: network embedding formulation on user-specific behavior networks, embedding weight distribution estimation through intermediate network layers, and a multi-factorization approach combining learned weights from implicit cross-domain data with explicit domain factors. Discover how this innovative method transfers knowledge across implicit and explicit factors, outperforming existing algorithms in real-world data tests. Gain insights into the potential of this approach for enhancing recommendation systems across various domains.

Network Embeddings based Recommendation Model with multi-factor consideration presented by ABHISHEK