Advanced New Technologies Co., Ltd.

Alibaba Group Holding Limited

Alipay (Hangzhou) Information Technology Co., Ltd.

Yuan Qi

Xiaofu Chang

Peng Xu

Le Song

Shen Li

Xu Yang

Wei Chu

Kaisheng Yao

Xiaoyan Jiang

Shaomeng Wang

Bin Dai

Jianfeng Wen

Zihao Fu

Xuqin Liu

Weiqiang Wang

Lingjuan Lv

The specification provides a method and a system for collaboratively updating a model by multiple parties for implementing privacy protection. A server can deliver an aggregation result of a t-th round of common samples to each participant i. Each participant i performs first update on a local ith model according to the t-th round of common samples and the aggregation result. Each participant i performs second update on the ith model obtained after the first update based on a first private sample fixed in a local sample set and a sample label thereof. Each participant i inputs a (t+1)th round of common samples that are used for a next round of iteration into the ith model obtained after the second update, and sends an output second prediction result to the server, so the server aggregates n second prediction results corresponding to n participants for a next round of iteration.

Methods, apparatuses, and systems for collaboratively updating model by multiple parties for implementing privacy protection

Embodiments of the present disclosure provide a method and system for training a model by using training data. The training data includes a plurality of samples, each sample includes N features, and features in the plurality of samples form N feature columns, and the method includes: determining an importance value of each of the N feature columns; determining whether the importance value of each of the N feature columns satisfies a threshold condition; performing a dimension reduction on M feature columns to generate P feature columns in response to the determination that the importance values of the M feature columns do not satisfy the threshold condition, wherein M&#x3c;N and P&#x3c;M; merging (N&#x2212;M) feature columns having importance values that satisfy the threshold condition and the generated P feature columns to obtain (N&#x2212;M+P) feature columns; and training the model based on the training data including the (N&#x2212;M+P) feature columns.

Method and system for training model by using training data

Data storage and calling methods and devices are provided. One of the methods includes: receiving first motion data and business data; establishing an association relationship between the first motion data and the business data and storing the association relationship; receiving second motion data; and determining first motion data that matches the second motion data, and returning, to a sender of the second motion data, business data associated with the matched first motion data.

Data storage and calling methods and devices

Computer-implemented methods, computer-implemented systems, and non-transitory, computer-readable media for processing interaction sequence data are disclosed. One computer-implemented method includes: obtaining a dynamic interaction graph is obtained, where the dynamic interaction graph is constructed based on a dynamic interaction sequence, including a plurality of interactions arranged in a chronological order, where each interaction includes two objects involved in the interaction and a time of the interaction. In the dynamic interaction graph, a sub-graph corresponding to a target node is determined, where nodes in the sub-graph comprise the target node and connection nodes connected to the target node through a predetermined amount of edges originating from the target node. A feature vector corresponding to the target node is determined based on a node feature of each of the nodes of the sub-graph and directions of edges of the sub-graph.

Method and apparatus for processing user interaction sequence data

The present disclosure provides feature data processing methods and devices. One exemplary feature data processing method comprises: classifying features into an important feature set and an auxiliary feature set according to information attribute values of the features; converting features in the auxiliary feature set to hash features; and combining the hash features with features in the important feature set, and setting the combined features as fingerprint features. Training and prediction of to-be-processed data can be performed based on the fingerprint features. With the embodiments of the present disclosure, training dimensions can be more controllable and the amount training data amount can be reduced. Therefore, the efficiency of data processing can be improved.

Feature data processing method and device

Growing community of inventors

Hangzhou, China

Yuan Qi