Big data prediction method for user's store location based on partial label learning

Abstract

The present invention claims to protect a large data prediction method for the location of the user's store based on partial label learning, including: 101 performing preprocessing operations on the user's shopping status data; 102 constructing a partial label data set according to the set of candidate stores corresponding to each sample ; 103 carry out the feature extraction operation on the partial label data set; 104 construct the similarity map according to the feature space; 105 carry out the probability propagation according to the similarity map; Stores where users will have behavioral interactions in the future. The present invention mainly preprocesses user historical data, extracts features, converts partial label data sets, and establishes partial label learning models. Predicting the stores that users will interact with in the future enables users to obtain more accurate personalized push services and improve users' shopping experience.

Description

technical field [0001] The invention belongs to the technical field of partial label learning and big data processing, and in particular is based on the prediction of big data of store location where a user is located based on a probability propagation model. Background technique [0002] Partial label learning is a weakly supervised learning in which the output space is associated with a set of candidate labels. Only one of the candidate label sets is the real label, and the remaining labels are regarded as interference noise labels. In the process of partial label training, the real label of each training sample is submerged in the candidate label set, so it is impossible to directly obtain the learning algorithm from the input space to the output space from the data set similar to the strong supervised learning. However, in real life, datasets with accurate and unique label information are more and more difficult to obtain. So we have to face the serious problem of how t...

AI Technical Summary

Problems solved by technology

In the process of partial label training, the real label of each training sample is submerged in the candidate label set, so it is impossible to obtain the learning algorithm from the input space to the output space directly from the data set similar to the strong supervised learning.

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