使用交替最小二乘优化来在线训练和更新因子分解机

• Format: Patent
• Language: Chinese
• Published: 10.01.2025
• Subjects: CALCULATING; COMPUTING; COUNTING; ELECTRIC DIGITAL DATA PROCESSING; PHYSICS

本公开的实施例涉及使用交替最小二乘优化来在线训练和更新因子分解机。公开了用于使用流模式交替最小二乘(ALS)优化来训练因子分解机(FM)的技术。实现根据实施例的技术的方法包括：接收包括特征向量和相关联的目标值的数据点。特征向量包括用户标识、主题标识和上下文。目标值标识用户对于主题的意见。该方法还包括将FM应用于特征向量以生成目标值的估计，并且更新FM的参数以用于FM的训练。参数更新基于流模式ALS优化到以下各项的应用：数据点；目标值的估计；以及通过将流模式ALS优化应用于先前接收的、与FM的先前参数更新相关联的数据点而生成的中间计算项的已更新总和。 Techniques are disclosed for training of factorization machines (FMs) using a streaming mode alternating least squares (ALS) optimization. A methodology implementing the techniques according to an embodiment includes receiving a datapoint that includes a feature vector and an associated target value. The feature vector includes user identification, subject matter identification, and a context. The target value identifies an opinion of the user relative to the subject matter. The method further includes applying an FM to the feature vector to generate an estimate of the target value, and updating parameters of the FM for training of the FM. The parameter update is based on application of a streaming mode ALS optimization to: the datapoin