Huayuan Liu

Flow-based Time-aware Causal Structure Learning for Sequential Recommendation

Sequential models aim to predict future interactions based on users' historical interaction sequences. Traditional sequential methods primarily focus on capturing intra-historical sequence dependencies, overlooking the influence of unobserved confounders in recommendation scenarios. Recent studies i