Cross-lingual Search Intent Understanding Framework Based on Multi-modal User Behavior

Abstract

This paper proposes a novel cross-lingual search intent understanding framework leveraging multi-modal user behavior analysis. With the increasing complexity of network traffic and the diversity of user behaviors across languages, traditional approaches often struggle to capture and interpret user search intent in multilingual contexts accurately. Our framework integrates multiple behavioral signals, including query patterns, click sequences, and temporal dynamics, through a sophisticated neural tensor network architecture. The system employs a dual-encoder structure with shared parameters to maintain semantic consistency across languages while incorporating a dynamic behavior sequence learning mechanism to capture temporal dependencies. Experimental evaluation was conducted on a large-scale dataset comprising over 6 million user interactions across four language pairs (EN-ZH, EN-ES, EN-FR, EN-DE) collected over six months. The framework significantly improves over baseline methods, demonstrating an average cross-lingual accuracy of 0.923 and behavior prediction precision of 0.891. Ablation studies reveal the critical role of multi-head attention mechanisms and temporal modeling in maintaining system performance. The framework retains real-time processing capabilities with an average latency of 45ms per request under standard load conditions. Our research advances the field of cross-lingual information retrieval by introducing a practical approach to integrating behavioral signals with linguistic features, providing valuable insights for developing more sophisticated multilingual search systems.