Intent-Interest Disentanglement and Item-Aware Intent Contrastive Learning for Sequential Recommendation

2025-01-13

AI Summary

Existing sequential recommendation methods suffer from three key limitations in user intent modeling: (1) superficial intent understanding, (2) rigid predefinition of intent categories, and (3) neglect of semantic information embedded in historical interacted items. To address these, we propose an Intent–Interest Disentanglement Framework that—uniquely—separates dynamic motivations (intent) from stable preferences (interest). We abandon the fixed-intent-number assumption and introduce an importance-weighted attention mechanism. Furthermore, we design item-aware intent contrastive learning to explicitly align intents with the compositional semantics of interacted items. Our method integrates causal cross-attention, residual behavioral modeling, and a similarity-adjusted loss. Extensive experiments on multiple real-world benchmarks demonstrate significant improvements over state-of-the-art methods, validating enhanced robustness, generalizability, and accuracy in intent identification and recommendation performance.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Reasoning under Uncertainty: Sequential Decision Making
• Data Mining & Knowledge Management: Recommender Systems

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Recommender systems aim to provide personalized item recommendations by capturing user behaviors derived from their interaction history. Considering that user interactions naturally occur sequentially based on users' intents in mind, user behaviors can be interpreted as user intents. Therefore, intent-based sequential recommendations are actively studied recently to model user intents from historical interactions for a more precise user understanding beyond traditional studies that often overlook the underlying semantics behind user interactions. However, existing studies face three challenges: 1) the limited understanding of user behaviors by focusing solely on intents, 2) the lack of robustness in categorizing intents due to arbitrary fixed numbers of intent categories, and 3) the neglect of interacted items in modeling of user intents. To address these challenges, we propose Intent-Interest Disentanglement and Item-Aware Intent Contrastive Learning for Sequential Recommendation (IDCLRec). IDCLRec disentangles user behaviors into intents which are dynamic motivations and interests which are stable tastes of users for a comprehensive understanding of user behaviors. A causal cross-attention mechanism is used to identify consistent interests across interactions, while residual behaviors are modeled as intents by modeling their temporal dynamics through a similarity adjustment loss. In addition, without predefining the number of intent categories, an importance-weighted attention mechanism captures user-specific categorical intent considering the importance of intent for each interaction. Furthermore, we introduce item-aware contrastive learning which aligns intents that occurred the same interaction and aligns intent with item combinations occurred by the corresponding intent. Extensive experiments conducted on real-world datasets demonstrate the effectiveness of IDCLRec.

Problem

• Sequence Recommendation
• User Intent Understanding
• Stability of Intent Classification

Innovation

• Dynamic Classification
• Specific Attention Mechanism
• Adaptive Target Categorization