Interest point check-in prediction method fusing deep learning with factorization machine

Abstract

The invention relates to an interest point check-in prediction method fusing deep learning with a factorization machine and belongs to the field of location check-in prediction. The method comprises the following steps: S1, acquiring check-in data of a user; S2, performing embedding processing on input discrete data; S3, performing sparse elimination processing on the discrete data, and learning implicit second order relations among the data; S4, learning addition of continuous characteristics into a full connection hidden layer, and selecting an appropriate excitation function; S5, inputtinga result obtained by processing discrete characteristics and a result obtained by processing the continuous characteristics and adding the results as an input of a hidden layer h1; S6, enabling an output l1 of the hidden layer h1 to pass a first-order linear and characteristic interaction structure and adding as an input of a hidden layer h2; and S7, receiving an input by a hidden layer h3 from outputs l1 and l2 of the hidden layers h1 and h2, adding a shortcut structure at the same time for guaranteeing gradient stability during parameter learning, determining the best model structure, and finally outputting a prediction result. The method provided by the invention fully excavates and learns check-in rules and predicts interest point check-in problems by analyzing check-in information ofthe user.

Description

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AI Technical Summary

Benefits of technology

This technology helps users see what they are doing well during their stay on points like restaurants or stores without being overheard by others who may have been looking around them too much about it. It will help reduce this problem because people only pay attention when there's something interesting happening near these locations instead of constantly checking up with each individual person every time.

Problems solved by technology

The problem addressed in this patented technology relates to accurately identifying specific locations or areas within a large area with multiple different types of sensors attached around it (such as cameras) while minimally affecting other nearby regions). This requires developing systems capable of capturing detailed mapings from all possible directions simultaneously without compromising accuracy.

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