LSTM Networks for Online Cross-Network Recommendations

Understand

Cross-network recommender systems use auxiliary information from multiple source networks to create holistic user profiles and improve recommendations in a target network.

• However, we find two major limitations in existing cross-network solutions that reduce overall recommender performance.
• Existing models (1) fail to capture complex non-linear relationships in user interactions, and (2) are designed for offline settings hence, not updated online with incoming interactions to capture the dynamics in the recommender environment.
• We propose a novel multi-layered Long Short-Term Memory (LSTM) network based online solution to mitigate these issues.