DTL-NS introduces hierarchical index trees and LLM inference on item-ID encodings to identify false negatives and perform multi-view hard negative sampling for improved implicit CF recommendation.
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cs.IR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
BST-CDSR combines neural ODEs for continuous behavioral preference modeling with LLM-based temporal semantic generation and adaptive domain transfer to improve cross-domain sequential recommendations.
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Dual-Tree LLM-Enhanced Negative Sampling for Implicit Collaborative Filtering
DTL-NS introduces hierarchical index trees and LLM inference on item-ID encodings to identify false negatives and perform multi-view hard negative sampling for improved implicit CF recommendation.
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Bridging Behavior and Semantics for Time-aware Cross-Domain Sequential Recommendation
BST-CDSR combines neural ODEs for continuous behavioral preference modeling with LLM-based temporal semantic generation and adaptive domain transfer to improve cross-domain sequential recommendations.