HiSAC: Hierarchical Sparse Activation Compression for Ultra-long Sequence Modeling in Recommenders
arXiv:2602.21009v1 Announce Type: cross Abstract: Modern recommender systems leverage ultra-long user behavior sequences to capture dynamic preferences, but end-to-end modeling is infeasible in production due …
Kun Yuan, Junyu Bi, Daixuan Cheng, Changfa Wu, Shuwen Xiao, Binbin Cao, Jian Wu, Yuning Jiang
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