Live shopping interestingness prediction method based on eye movement features and DeepFM

A prediction method and eye movement feature technology, applied in the field of recommendation system, to achieve the effect of enhancing reliability and accuracy

Pending Publication Date: 2022-07-05
DONGHUA UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

The technical effect described by this patented technology for predictive analysis on shoppers' movements during online purchases involves applying eyes movement factors (FIs), which are used to fill up existing datasets with relevant details about how people behave while they shop at stores or other locations around them. This helps identify patterns that may indicate potential future purchase activity without actually visiting storefronts. Additionally, it suggests integrating advanced models like convolutional neural networks into their framework to improve its performance over older methods such as regression techniques. Overall, these technologies help businesses make better informed decisions based upon customers’ behavior rather than just looking backward from past transactions.

Problems solved by technology

Technological Problem addressed in this patents relates to studying how individuals see things they like while doing something online without actually visiting storefront locations where customers watch television shows during real world shops. Current approaches involve analyzing viewer behavior with limited input sources, making it hard to accurately predict what interests will happen next time before purchasing items through virtual reality experiences. Existing focuses on improving media attention and engagement show promise towards developing new ways to promote living streamings.

Method used

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  • Live shopping interestingness prediction method based on eye movement features and DeepFM
  • Live shopping interestingness prediction method based on eye movement features and DeepFM
  • Live shopping interestingness prediction method based on eye movement features and DeepFM

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Embodiment Construction

[0025] The purpose of the present invention is to solve the problems existing in the prior art, and provide a method for predicting the interest degree of live shopping based on eye movement characteristics and DeepFM. The technical scheme specifically adopted in the present invention is as follows:

[0026] like figure 1 Shown is the flow chart of the technical solution, which specifically includes the following steps:

[0027] S1. Perform gaze time-related data processing on the live video output by the eye tracker. Use the packaged tracking model to track live sales items. The tracking frame is established as the user's viewpoint and the target area. When the target area covers the user's viewpoint, it is determined that the area is coincident, that is, the user's viewpoint is paying attention to the area within the corresponding time.

[0028] Step S1 specifically includes the following steps:

[0029] S11. Use random samples in the video sequence for training, that is, e

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PUM

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Abstract

The invention discloses a live shopping interestingness prediction method based on eye movement characteristics and DeepFM. Data processing related to fixation time is carried out on a live video output by an eye tracker. And tracking the live selling goods by using the packaged tracking model. And establishing a tracking frame as a user viewpoint and a target area. On the basis of the obtained eye movement data of the user, a cooperation information graph is introduced, and user behaviors and project knowledge are coded into a unified relation graph through the cooperation information graph; based on a DeepFM architecture, adding a self-attention mechanism on a deep neural network to improve the learning ability of the model for key information; and outputting a result, and judging the precision of the model through a binary cross entropy loss function Loss and an AUC. A model is trained based on historical browsing data (including eye movement data) of a user. A user can predict the interestingness of the user for live broadcast commodities through the model, so that related personnel can adjust a live broadcast strategy, and the user experience in live broadcast is improved.

Description

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Claims

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Application Information

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Owner DONGHUA UNIV
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