Papers by Weihao Han

4 papers
To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion (2023.acl-long)

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Challenge: Existing methods for embedding knowledge graphs implicitly memorize relation rules to infer missing links, but they are difficult to memorize due to the inherent deficiencies of such implicit memorization strategy.
Approach: They propose a vertical learning paradigm that allows to explicitly copy target information from related factual triples for more accurate prediction.
Outcome: The proposed model improves generalization ability and makes distant link prediction significantly easier.
DUET: Joint Exploration of User–Item Profiles in Recommendation System (2026.findings-acl)

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Challenge: Existing LLMs are opaque and difficult to interpret, resulting in limited interpretability.
Approach: They propose an interaction-aware profile generator that jointly produces user and item profiles conditioned on both user history and item evidence.
Outcome: The proposed model outperforms baselines on three real-world datasets.
MRT: Multi-modal Short- and Long-range Temporal Convolutional Network for Time-sync Comment Video Behavior Prediction (2024.lrec-main)

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Challenge: Using time-sync comments, it is difficult to understand user behavior due to complexity of interactions between users, videos, and comments.
Approach: They propose a novel time-sync comment behavior prediction model that takes historical behavior into account and optimizes it on the basis of user preferences.
Outcome: The proposed model improves the performance of time-sync comments on visual frames and textual comments on two cats playing simultaneously.
Alleviating Performance Degradation Caused by Out-of-Distribution Issues in Embedding-Based Retrieval (2025.findings-emnlp)

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Challenge: Recent studies reveal query out-of-distribution issues degrading ANN performance . a distribution regularizer is introduced into the encoder training objective to encourage alignment between query and base embeddings.
Approach: They introduce a distribution regularizer into the encoder training objective to encourage alignment between query and base embeddings.
Outcome: The proposed method consistently improves retrieval performance across multiple datasets.

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