A Microwave Oven Dish Recommendation System Based on Multi-Perspective Dialogue-Recommended Dual-Stream Contrast Fusion

When people are uncertain about choosing micro-wave oven dishes, an effective recommendation system with high commercial value is essential. However, existing systems typically provide recommendations from a single perspective based on users' historical data, which limits their ability to fully understand users' real needs and preferences for different items. To address this, we propose Dual-MDR, a microwave oven dish recommendation system based on Multi-perspective Dialogue-Recommended Dual-stream contrastive fusion. Dual-MDR integrates recommendation and dialogue modules, utilizing a Multi-perspective Dual-stream contrastive Fusion (MDF) strategy to improve recommendation accuracy. In the recommendation module, we introduce an Attention-based Latent Factor Model (LFMAtt) to distinguish users' preferences for different dishes and deliver more accurate recommendations. In the dialogue module, we incorporate the MSIM-SOG model to capture users' individual needs. The MDF strategy fuses users' specific preferences with their historical data through multi-perspective dual-stream information integration. Additionally, contrastive learning is employed to enhance accuracy. Through extensive experiments and real-world deployments, we evaluated the system's performance. Feedback from 257 users shows an impressive 87.16<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> satisfaction rate, demonstrating its practical value and strong user acceptance for continued use.