Lifang Wu

Knowledge-guided interest co-teaching for personalized recommendation

LLM-Summarized Interest Distillation for Multimedia Recommendation

Recommendation system technology faces the fundamental challenge of data sparsity. Although incorporating auxiliary textual data works to enrich interactions, lengthy and unstructured item descriptions often fail to depict user interests, as they primarily describe item attributes. Furthermore, the sparse interactions...

PL-NBA: A Possession-level Universal Basketball Video Dataset Supporting Multiple Visual Understanding Tasks

Visual understanding in sports has emerged as a hot topic in computer vision in recent years. Most existing basketball video datasets adopt single action or activity as sample, which can neither preserve the temporal continuity of game events nor support complex tasks such as action anticipation. To address this issue,...

NBA_Streaming: A Large-Scale Benchmark for Fine-Grained Basketball Commentary Generation in Continuous Streams

This work introduces NBA_Streaming, a large-scale benchmark for online fine-grained basketball commentary generation and proposes a causal two-stage framework that combines completion-first localization with ball-centric semantic grounding, enabling the system to identify complete events from observed streams and organ...