Apple 用混合文本与 ID 嵌入实现增量视频搜索个性化排序

Apple 提出一种混合文本与 ID 嵌入方法，用于增量视频搜索中每次按键后的高质量排序。

该方法在意图不明确（如仅输入 1-3 个字符）时仍能提升个性化排序效果。

研究来自 Apple Machine Learning Research，未公布具体 benchmark 分数或可用性细节。

Personalizing Incremental Video Search with Hybrid Text and ID Embeddings

Authors Vivek Kanojiya, Vishalaksh Aggarwal, Daeho Baek, Lyndon Kennedy, Xuetao Yin

View publication：https://arxiv.org/abs/2607.13493

BayesCNS: A Unified Bayesian Approach to Address Cold Start and Non-Stationarity in Search Systems at Scale

December 11, 2024 research area Knowledge Bases and Search：/research/?domain=Knowledge%20Bases%20and%20Search, research area Methods and Algorithms：/research/?domain=Methods%20and%20Algorithms conference AAAI：/research/?event=AAAI

Information Retrieval (IR) systems used in search and recommendation platforms frequently employ Learning-to-Rank (LTR) models to rank items in response to user queries. These models heavily rely on features derived from user interactions, such as clicks and engagement data. This dependence introduces cold start issues for items lacking user engagement and poses challenges in adapting to non-stationary shifts in user behavior over time. We…

Consistent Collaborative Filtering via Tensor Decomposition

August 16, 2023 research area Knowledge Bases and Search：/research/?domain=Knowledge%20Bases%20and%20Search, research area Methods and Algorithms：/research/?domain=Methods%20and%20Algorithms

Collaborative filtering is the de facto standard for analyzing users’ activities and building recommendation systems for items. In this work we develop Sliced Anti-symmetric Decomposition (SAD), a new model for collaborative filtering based on implicit feedback. In contrast to traditional techniques where a latent representation of users (user vectors) and items (item vectors) are estimated, SAD introduces one additional latent vector to each…

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