{"@context":"https://schema.org","@type":"NewsArticle","generatedAt":"2026-07-23T06:40:50.084Z","headline":"Apple 用混合文本与 ID 嵌入实现增量视频搜索个性化排序","description":"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。","url":"https://www.aioga.com/news/cmro5cp3e00qqbiknxf65hpkg/","mainEntityOfPage":"https://www.aioga.com/news/cmro5cp3e00qqbiknxf65hpkg/","datePublished":"2026-07-16T00:00:00.000Z","dateModified":"2026-07-16T00:00:00.000Z","inLanguage":"zh-CN","publisher":{"@type":"NewsMediaOrganization","name":"Aioga","url":"https://www.aioga.com"},"citation":["https://machinelearning.apple.com/research/personalizing-incremental-video-search","https://aihot.virxact.com/items/cmro5cp3e00qqbiknxf65hpkg"],"canonicalUrl":"https://www.aioga.com/news/cmro5cp3e00qqbiknxf65hpkg/","directAnswer":{"@type":"Answer","text":"Aioga 编辑摘要：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。 Aioga 将其归入「论文研究」方向，重点关注它对真实使用和行业竞争的影响。","url":"https://www.aioga.com/news/cmro5cp3e00qqbiknxf65hpkg/","dateCreated":"2026-07-16T00:00:00.000Z","author":{"@type":"Organization","@id":"https://www.aioga.com/authors/aioga-editorial/#editorial-team","name":"Aioga Editorial Team","url":"https://www.aioga.com/authors/aioga-editorial/"}},"evidence":[{"@type":"CreativeWork","name":"machinelearning.apple.com source article","url":"https://machinelearning.apple.com/research/personalizing-incremental-video-search","datePublished":"2026-07-16T00:00:00.000Z","provider":{"@type":"Organization","name":"machinelearning.apple.com","url":"https://machinelearning.apple.com/research/personalizing-incremental-video-search"}},{"@type":"CreativeWork","name":"AIHot archive record","url":"https://aihot.virxact.com/items/cmro5cp3e00qqbiknxf65hpkg","datePublished":"2026-07-16T00:00:00.000Z","provider":{"@type":"Organization","name":"AIHot","url":"https://aihot.virxact.com/items/cmro5cp3e00qqbiknxf65hpkg"}}],"aggregationSource":"Apple Machine Learning Research（RSS）","originalPublisher":{"name":"machinelearning.apple.com","url":"https://machinelearning.apple.com/research/personalizing-incremental-video-search"},"article":{"id":"cmro5cp3e00qqbiknxf65hpkg","slug":"cmro5cp3e00qqbiknxf65hpkg","url":"https://www.aioga.com/news/cmro5cp3e00qqbiknxf65hpkg/","title":"Apple 用混合文本与 ID 嵌入实现增量视频搜索个性化排序","title_en":"Personalizing Incremental Video Search with Hybrid Text and ID Embeddings","summary":"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。","source":"Apple Machine Learning Research（RSS）","sourceUrl":"https://machinelearning.apple.com/research/personalizing-incremental-video-search","aiHotUrl":"https://aihot.virxact.com/items/cmro5cp3e00qqbiknxf65hpkg","publishedAt":"2026-07-16T00:00:00.000Z","category":"论文研究","score":42,"selected":false,"articleBody":["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. 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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。","category":"论文研究","source":"Apple Machine Learning Research（RSS）","pageTitle":"Apple 用混合文本与 ID 嵌入实现增量视频搜索个性化排序 - Aioga Notizie IA","description":"Aioga tracks this update from Apple Machine Learning Research（RSS） under 论文研究. 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 L","url":"https://www.aioga.com/it/news/cmro5cp3e00qqbiknxf65hpkg/"},"nl":{"title":"Apple 用混合文本与 ID 嵌入实现增量视频搜索个性化排序","summary":"Aioga tracks this update from Apple Machine Learning Research（RSS） under 论文研究. 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。","category":"论文研究","source":"Apple Machine Learning Research（RSS）","pageTitle":"Apple 用混合文本与 ID 嵌入实现增量视频搜索个性化排序 - Aioga AI-nieuws","description":"Aioga tracks this update from Apple Machine Learning Research（RSS） under 论文研究. 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 L","url":"https://www.aioga.com/nl/news/cmro5cp3e00qqbiknxf65hpkg/"},"tr":{"title":"Apple 用混合文本与 ID 嵌入实现增量视频搜索个性化排序","summary":"Aioga tracks this update from Apple Machine Learning Research（RSS） under 论文研究. 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。","category":"论文研究","source":"Apple Machine Learning Research（RSS）","pageTitle":"Apple 用混合文本与 ID 嵌入实现增量视频搜索个性化排序 - Aioga AI Haberleri","description":"Aioga tracks this update from Apple Machine Learning Research（RSS） under 论文研究. 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 L","url":"https://www.aioga.com/tr/news/cmro5cp3e00qqbiknxf65hpkg/"},"vi":{"title":"Apple 用混合文本与 ID 嵌入实现增量视频搜索个性化排序","summary":"Aioga tracks this update from Apple Machine Learning Research（RSS） under 论文研究. 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。","category":"论文研究","source":"Apple Machine Learning Research（RSS）","pageTitle":"Apple 用混合文本与 ID 嵌入实现增量视频搜索个性化排序 - Tin tức AI Aioga","description":"Aioga tracks this update from Apple Machine Learning Research（RSS） under 论文研究. 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 L","url":"https://www.aioga.com/vi/news/cmro5cp3e00qqbiknxf65hpkg/"},"id":{"title":"Apple 用混合文本与 ID 嵌入实现增量视频搜索个性化排序","summary":"Aioga tracks this update from Apple Machine Learning Research（RSS） under 论文研究. 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。","category":"论文研究","source":"Apple Machine Learning Research（RSS）","pageTitle":"Apple 用混合文本与 ID 嵌入实现增量视频搜索个性化排序 - Berita AI Aioga","description":"Aioga tracks this update from Apple Machine Learning Research（RSS） under 论文研究. 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 L","url":"https://www.aioga.com/id/news/cmro5cp3e00qqbiknxf65hpkg/"},"th":{"title":"Apple 用混合文本与 ID 嵌入实现增量视频搜索个性化排序","summary":"Aioga tracks this update from Apple Machine Learning Research（RSS） under 论文研究. 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。","category":"论文研究","source":"Apple Machine Learning Research（RSS）","pageTitle":"Apple 用混合文本与 ID 嵌入实现增量视频搜索个性化排序 - ข่าว AI Aioga","description":"Aioga tracks this update from Apple Machine Learning Research（RSS） under 论文研究. 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 L","url":"https://www.aioga.com/th/news/cmro5cp3e00qqbiknxf65hpkg/"},"pl":{"title":"Apple 用混合文本与 ID 嵌入实现增量视频搜索个性化排序","summary":"Aioga tracks this update from Apple Machine Learning Research（RSS） under 论文研究. 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。","category":"论文研究","source":"Apple Machine Learning Research（RSS）","pageTitle":"Apple 用混合文本与 ID 嵌入实现增量视频搜索个性化排序 - Aioga Wiadomości AI","description":"Aioga tracks this update from Apple Machine Learning Research（RSS） under 论文研究. 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 L","url":"https://www.aioga.com/pl/news/cmro5cp3e00qqbiknxf65hpkg/"}}}}