Wang Jiaheng

Length-Adaptive Interest Network for Balancing Long and Short Sequence Modeling in CTR Prediction

User behavior sequences in modern recommendation systems exhibit significant length heterogeneity, ranging from sparse short-term interactions to rich long-term histories. While longer sequences provide more context, we observe that increasing the maximum input sequence length in existing CTR models