arXiv：cs.LG（机器学习，全量分类）· Parthiv Chatterjee, Dhiraj Golhar, Ummesalma Diwan, Sourish Dasgupta, Manjunath Joshi, Tanmoy Chakraborty·· 15 小时前AI 评分38
REPAIR：修复个性化编码器有损用户偏好状态
Not All Is Lost: Repairing Lossy User Preference States of Personalization Encoders
AI 导读
研究者提出 REPAIR，通过在学习到的紧凑坐标空间中对比缓存表示与当前偏好状态，为个性化编码器补回被压缩丢失的偏好证据。
正文
Abstract:Personalization encoders compress evolving interaction histories into preference states used to rank items or condition text generation. A task head operating only on this state can miss useful evidence that remains in the frozen encoder's cached representations for individual timesteps. We study this recoverability gap and propose REPAIR, which compares cached representations with the current preference state in a compact learned coordinate space. It resolves corrective evidence over extended history, recent interactions, and localized bursts. It then selects which patterns at which timesteps contribute and adds their aggregate correction to the state before the task head. Encoder-host repair reuses representations from the existing forward computation without re-encoding the history. Across MovieLens, PENS, MIND, and Amazon Reviews 2023, training only REPAIR improves MRR and nDCG@10 for all twelve representative recommendation hosts while both encoder and task head remain frozen. Head-only finetuning of the same hosts yields smaller gains. For example, Mamba4Rec on MovieLens gains 3.96 MRR points, compared with 0.19 from head-only finetuning. Rank and temporal diagnostics support a compact, host-dependent corrective structure. In personalized generation, IMPerSumm improves the two reported weighted PerSEval variants, which assess responsiveness to user preference, by up to 25.23%. These results support post-compression state correction and distinguish the availability of preference evidence from its downstream use.
| Comments: | Accepted to NeurIPS 2026. Author-prepared archival version with expanded discussion and interpretation. 59 pages, including references and appendices |
| Subjects: | Machine Learning (cs.LG); Information Retrieval (cs.IR) |
| Cite as: | arXiv:2610.01270 [cs.LG] |
| (or arXiv:2610.01270v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.01270 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Sourish Dasgupta [view email]
[v1]
Thu, 1 Oct 2026 08:09:37 UTC (1,343 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org