Conversational Recommendation Training With Pseudo-User Interest Models

The patent applies preliminary action by pre-training the recommendation system with synthetic pseudo-user data before actual deployment. This pre-training phase creates a foundational model that can provide reasonable recommendations immediately, while subsequent fine-tuning with real user data progressively improves accuracy. The system performs the useful action of learning recommendation patterns in advance, reducing the cold start problem and minimizing interactions needed for accurate recommendations.

The patent applies partial action by implementing a multi-stage training approach where the system learns from synthetic data first, then progressively refines with real data. This allows the system to achieve sufficient recommendation accuracy with fewer interactions by leveraging the preliminary knowledge gained from synthetic pseudo-user conversations. The system performs enough learning action in advance to reduce the need for extensive real-time interactions.

The patent applies the intermediary principle by introducing synthetic pseudo-user data as a mediator between the absence of real data and the need for domain-specific training. This intermediary training data allows the system to learn domain-specific patterns without requiring immediate access to large amounts of real user data or complex data collection infrastructure. The synthetic data serves as a bridge that simplifies the training process while still achieving domain-specific accuracy.

The patent applies copying by creating synthetic pseudo-user data that replicates the statistical patterns and conversational structures of real user data without containing actual user information. This synthetic copy allows the training process to learn from data that mimics real user behavior while eliminating privacy risks associated with using actual user data. The system learns from copies of user interaction patterns rather than from real user data itself.