研究人员推出了一种新颖的贝叶斯混合模型BACH，旨在改进多兴趣双塔检索系统。与可能遭受路由崩溃和用户兴趣头利用不足的现有方法不同，BACH采用通过变分推断训练的软混合方法。该方法确保所有兴趣头都得到训练，并为服务提供每用户兴趣权重，且在大规模基准测试（包括MovieLens-20M、Taobao和Netflix）上已证明了其卓越的性能。 AI
影响 提高处理多样化用户兴趣的系统的检索准确性和效率。
排序理由 该集群描述了一篇关于新颖检索模型的学术论文。
在 arXiv cs.IR (Information Retrieval) 阅读 →
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
完整方法见我们的编辑标准。
arXiv:2607.08107v1 Announce Type: cross Abstract: Two-tower retrievers compress each user into a single embedding, limiting their ability to serve diverse interests. Multi-interest models give each user several heads scored by a maximum inner product, but their hard-routing train…
Two-tower retrievers compress each user into a single embedding, limiting their ability to serve diverse interests. Multi-interest models give each user several heads scored by a maximum inner product, but their hard-routing training under-utilizes heads (routing collapse) and gi…