New method improves long sequence item recommendations with better speed and accuracy
Preference-Drift-Aware Subsequence Learning and Hierarchical Context Fusion for Long-Sequence Generative Recommendation
Information Retrieval
Summary
Many recommendation systems try to guess what you’ll like next by looking at everything you've done before, but this can be slow and sometimes less accurate when there’s a lot of history. The authors found that current methods either take too long with long histories or don’t handle changes in your tastes well. They created a new approach that splits your activity into smaller chunks and combines recent and long-term preferences in a smart way. This makes recommendations both faster to compute and better at matching what you want now.
What this means in practice
- •For ecommerce platform engineers: Improve product recommendation quality and server efficiency by adapting to users’ changing preferences over long interaction histories.
- •For video streaming service developers: Deliver more accurate next-item suggestions by better modeling users’ shifting tastes through hierarchical context and subsequence learning.
Authors
Fei Li, Qingyun Gao, Jianzhe Zhao, Guibing Guo, Beibei Kong, Lei Cheng, Chengxiang Zhuo, Zang Li
Abstract
Long-sequence generative recommendation methods autoregressively model the user's interaction sequence to generate the next-item representation. Existing methods generally fall into two categories: efficient full-sequence modeling and target-aware context retrieval. Our experiments reveal that as the sequence length increases, the former incurs steadily growing computational cost while its accuracy gains quickly saturate and even degrade due to noise; the latter, though shortening the input sequence, is susceptible to noise that is semantically consistent yet preference-inconsistent, as well as to incomplete contexts. Both paradigms ignore the dynamic changes of user preferences and the cross-subsequence dependencies when handling historical information, thereby limiting accuracy and efficiency. To address these issues, we propose a preference-drift-aware subsequence learning and hierarchical context fusion for long-sequence generative recommendation. Specifically, we learn differentiable soft subsequence boundaries using multidimensional preference-drift information and aggregate items within each subsequence into preference-coherent representations via linear attention with soft assignment weights, thereby circumventing the expense of full-sequence attention. A cross-attention mechanism is then employed to capture dependencies between recent interactions and relevant subsequence contexts, mitigating noise in learning recent-item representations. Finally, a gated fusion mechanism adaptively combines the recent-item representation with the global subsequence context, allowing the resulting target representation to encode both recent and long-term preferences. Extensive experiments demonstrate that our method consistently outperforms existing baselines in both recommendation accuracy and computational efficiency.