Content-based user personalized product matching recommendation method

A recommendation method and user's technology, applied in data processing applications, business, instruments, etc., can solve problems such as short training time

Active Publication Date: 2019-11-05
XIDIAN UNIV
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  • Application Information

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Problems solved by technology

[0005] The present invention overcomes the problem that the recommendation algorithm still needs to be improved in the p

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  • Content-based user personalized product matching recommendation method
  • Content-based user personalized product matching recommendation method
  • Content-based user personalized product matching recommendation method

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Embodiment Construction

[0060] The content-based user-personalized product matching and recommendation method of the present invention is further described below in conjunction with the accompanying drawings and specific embodiments: The specific implementation steps of the user-based random batch sampling method are as follows:

[0061] Step 1: Sort out interaction information files, user information files, and product information files, and divide test sets and training sets.

[0062] The data set used should include no less than 100,000 user-product interaction records and corresponding user personal information and product content information; taking the public data set MovieLens 100k movie recommendation data set as an example, the data set contains 943 users (1-943 ) for the rating (1-5) data of 1682 movies (1-1682), each user has at least 20 evaluation records, and three files are sorted out from the data set used:

[0063] ratings: Each row of data includes [user id, movie id, rating];

[00...

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Abstract

The invention discloses a content-based user personalized product matching recommendation method. The problem that in the prior art, a recommendation algorithm still needs to be improved is solved. The method comprises the following steps: step 1, a random batch sampling method based on a user; step 2, a content-based user product matching method: establishing a network; according to a batch inputuser-based random batch sampling method, obtaining an ordered set of a user id, a historical record product id list, a target product id and label set, and training, adjusting and evaluating a network model on the training set, the verification set and the test set respectively; and inputting a specific user and historical records thereof, predicting scores of the specific user for all unwatchedmovies by using a content-based user product matching network, sorting the scores, and finally outputting a top-N recommendation result. According to the method, the lightweight neural network is adopted, so that the training time and training equipment requirements are greatly reduced, the sampling process is easy, the model input is more random, and the generalization ability of the prediction result is stronger.

Description

technical field [0001] The invention relates to the field of recommendation algorithms, in particular to a content-based user personalized product matching recommendation method. Background technique [0002] With the development of the Internet era, the phenomenon of information overload is becoming more and more serious, users are faced with more and more product choices, and the competition between products is increasing. A recommendation algorithm is an algorithm that matches users and products. A good recommendation algorithm can not only save user time and increase user satisfaction, but also increase the acceptance rate of products and increase transaction volume. [0003] Existing recommendation algorithms generally obtain product latent factors and user latent factors from the user-product interaction information matrix in advance through collaborative filtering algorithms, and then use the pre-trained latent factors to train the recommendation model to obtain reco...

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Application Information

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IPC IPC(8): G06Q30/06G06Q30/02
CPCG06Q30/0631G06Q30/0202
Inventor 宋彬马梦迪
Owner XIDIAN UNIV
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