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METHODS AND APPARATUSES FOR INFORMATION PROCESSING AND INFORMATION RECOMMENDATION ELECTRONIC DEVICE STORAGE MEDIUM AND COMPUTER PROGRAM PRODUCT

机译:用于信息处理和信息推荐电子设备存储介质和计算机程序产品的方法和装置

摘要

The present disclosure discloses an information processing method, an information processing apparatus, an information recommendation method, an information recommendation apparatus, an electronic device, and a storage medium, and relates to the field of artificial intelligence, in particular, a recommendation system and a deep learning technology field. In the information processing method of the present disclosure, the computing device obtains a tree structure parameter of a tree structure, and the tree structure is for indexing a set of objects for recommendation. The computing device obtains a classifier parameter of the classifier, and the classifier is for predicting a set of preferred nodes in which the rank of the probability preferred by the user in each layer is preceded by one layer down from the top layer of the tree structure, where the one layer The preferred node set of ' is determined based on the preferred node set of the upper layer. The computing device builds a recall model based on the tree structure parameter and the classifier parameter. The recall model constructed according to an embodiment of the present disclosure uses the tree structure parameter and the classifier parameter as model parameters to optimize the training process and use process of the recall model, thereby improving the recall result of the recall model.
机译:本公开公开了一种信息处理方法,信息处理装置,信息推荐方法,信息推荐装置,电子设备和存储介质,并且涉及人工智能领域,特别是推荐系统和A深度学习技术领域。在本公开的信息处理方法中,计算设备获得树结构的树结构参数,树结构用于索引一组对象以供推荐。计算设备获取分类器的分类器参数,并且分类器用于预测一组优选节点,其中每个层中优选的用户优选的概率的等级是从树结构的顶层下方的一层之前。 ,基于上层的优选节点组确定'优选节点集的一个层。计算设备基于树结构参数和分类器参数构建召回模型。根据本公开的实施例构造的召回模型使用树结构参数和分类器参数作为模型参数以优化召回模型的训练过程和使用过程,从而改善召回模型的召回结果。

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