Image recommendation method merging visual features and user ratings

A technology of visual features and recommendation methods, applied in the field of image recommendation through matrix decomposition, can solve problems such as the inability to consider the user's personal preferences and interests, and the inability to realize personalized recommendations, and achieve improved recommendation accuracy, wide application range, and convergence effect Good results

Active Publication Date: 2018-04-20
合肥微木秉智科技有限公司
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Problems solved by technology

Moreover, this traditional image recommendation only focuses on the attributes of the item itself, and cannot take

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  • Image recommendation method merging visual features and user ratings
  • Image recommendation method merging visual features and user ratings
  • Image recommendation method merging visual features and user ratings

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Example Embodiment

[0037] In this embodiment, an image recommendation method that integrates visual features and user ratings includes: crawling a data set and extracting item images from the data set and a user's rating matrix for the corresponding item images, and using roll-ups on the collected item images The product neural network extracts the visual features of the image, obtains the visual feature matrix, establishes a predictive preference model, updates the predictive preference model using the element-based alternating least squares method, and obtains the user's preference value for all item images from the final predictive preference model, complete Image recommendation. The overall process is as figure 1 As shown, specifically, follow the steps below

[0038] Step 1. Use a web crawler to crawl the item image set P and the corresponding item score data set Q from the website;

[0039] Step 1.1. Initialize the URL list;

[0040] Step 1.2, call the API to obtain a large amount of product i...

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Abstract

The invention discloses an image recommendation method merging visual features and user ratings. The method includes the first step of crawling a data set and extracting item images from the data setand a user's scoring matrix of the corresponding item images; the second step of utilizing a convolutional neural network (CNN) to extract image visual features of all the collected item images, to obtain a visual feature matrix; the third step of establishing a prediction preference model, and using an element-based alternating least squares method to update the prediction preference model; the fourth step of obtaining the user's preference values of all the item images from the final prediction preference model, sorting the preference values in descending order, selecting item images corresponding to top preference values, and recommending the item images to the user. The image recommendation method merges the visual features and the user ratings so that recommendation accuracy can be improved and personalized recommendations can be achieved.

Description

technical field [0001] The invention belongs to the technical field of image processing based on computer vision technology, and mainly relates to an image recommendation method of matrix decomposition. Background technique [0002] In recent years, with the rapid development of e-commerce, a large amount of network image data has been generated. Faced with such a large amount of image data, users hope to quickly locate the image information they are interested in, and search has become a must for this purpose. function, while search is a service request initiated by the user. In order to allow the system to actively provide services for customers, an image recommendation system has emerged, which recommends images for users by analyzing the historical data that users are interested in and the image data in the image database. The most likely image content in the database is the image content that the user is interested in, that is, the image that is closest to the image tha...

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Application Information

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IPC IPC(8): G06F17/30G06Q30/06G06K9/46
CPCG06F16/9535G06Q30/0631G06V10/40
Inventor 薛峰孙健陈思洋路强余烨
Owner 合肥微木秉智科技有限公司
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