Enterprise AI Analysis
STEP: Stepwise Curriculum Learning for Context-Knowledge Fusion in Conversational Recommendation
This paper introduces STEP, a novel conversational recommender system (CRS) that enhances recommendation accuracy and dialogue quality by integrating pre-trained language models (PLMs) with knowledge graphs (KGs) through a curriculum-guided context-knowledge fusion module (F-Former) and lightweight task-specific prompt tuning. STEP addresses challenges in capturing deep semantics of user preferences and dialogue context by progressively aligning dialogue context with KG entities across a three-stage curriculum. Experimental results on two public datasets demonstrate STEP's superior performance over state-of-the-art methods in both recommendation precision and dialogue quality.
Executive Impact
STEP's innovative approach drives significant improvements in core metrics, ensuring more relevant recommendations and richer user interactions for conversational AI platforms.
+15.7%
Recall@1 Improvement (ReDial)
+40.8%
Recall@1 Improvement (INSPIRED)
+20%
Dialogue Diversity Increase (Distinct-2)
Deep Analysis & Enterprise Applications
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F-Former Architecture
The F-Former module is a transformer-based alignment mechanism that projects graph-derived features into the PLM's semantic space. It uses learnable cross-modal queries and a coarse-to-fine scheduling strategy to drive contextual fusion, progressively bridging the semantic gap between dialogue and knowledge graphs.
Curriculum Learning
STEP employs a three-stage curriculum (Contrastive Warm-Up, Triplet Refinement, Auxiliary Matching Consolidation) to ensure stable training and guide the model from coarse-grained to fine-grained semantic alignment. This progressive approach enhances the fusion of dialogue context and knowledge graph semantics.
Prompt Tuning
Lightweight task-specific prompt tuning injects fused context-knowledge embeddings into the frozen language model via dual-prefix prompts: a conversation prefix for response generation and a recommendation prefix for item ranking. This allows shared cross-task semantics while respecting distinct objectives.
+15.7%
Recall@1 improvement on ReDial dataset for recommendation accuracy with STEP over baselines.
STEP's F-Former Curriculum Learning Process
Information Encoding (Dialogue & KG)
→
Batch-hard Contrastive Learning
→
Triplet Refinement (Query-Label)
→
Auxiliary Query-Label Matching
→
Fused Representation
| Feature | Traditional CRSs | STEP |
|---|---|---|
| Context-Knowledge Fusion |
|
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| PLM Integration |
|
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| Recommendation Accuracy |
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Example: Movie Recommendation with STEP
Scenario: A user asks for 'sci-fi film like Blade Runner'.
Traditional CRSs Response: Traditional systems might suggest 'The Matrix Resurrections' (popular sci-fi, but not a direct sequel or similar tone).
STEP's Response: STEP leverages its F-Former and KG integration to identify 'Blade Runner 2049' (the official sequel), demonstrating effective capture of nuanced user intent.
Analysis: This shows STEP's ability to fuse KG relations and dialogue context, accurately capturing 'classic' and 'suspense' attributes for highly relevant recommendations, unlike traditional CRSs that miss subtle cues.
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