摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-根据来自华盛顿特区的新闻报道来源，由NewsRx记者，Inve Ntors GONG，Xiaohong(加利福尼亚州桑尼维尔)；PARASRAMPURIA，Nipun(旧金山，加利福尼亚州)；PETROVIC，Nemanja(贝尔格莱德，RS)；Rahupathi，Nishanth Rao，2022年11月29日提交的，于2024年5月30日在线提供。本专利申请的受让人是Roblox Corporation(San Mateo,Calif Ornia,United States)。记者从发明人提供的背景信息中获得了以下引文：“当已知用户的信息并且知道关于体验的信息时，推荐系统非常擅长向用户推荐经验。推荐系统可以包括用分层可微模型从标记数据中进行高级再表示。但是，推荐系统往往效率低下，数据缺乏鲁棒性，和/或数据不可泛化。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-According to news reporting originatin g from Washington, D.C., by NewsRx journalists, a patent application by the inve ntors GONG, Xiaohong (Sunnyvale, CA, US); PARASRAMPURIA, Nipun (San Francisco, C A, US); PETROVIC, Nemanja (Belgrade, RS); RAGHUPATHI, Nishanth Rao Palimar (Cast ro Valley, CA, US); SHAHIDI, Shervin (Foster City, CA, US); SUN, Xusheng (Sunnyv ale, CA, US), filed on November 29, 2022, was made available online on May 30, 2 024. The assignee for this patent application is Roblox Corporation (San Mateo, Calif ornia, United States). Reporters obtained the following quote from the background information supplied by the inventors: "Recommendation systems are excellent at recommending experien ces to users when information is known about the users and when information is k nown about the experiences. The recommendation systems may include high-level re presentations from labeled data with layered differentiable models. However, the recommendation systems are often inefficient, the data lacks robustness, and/or the data is not generalizable.
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