An article score prediction method based on matrix decomposition and neural collaborative filtering

A collaborative filtering and matrix decomposition technology, applied in neural learning methods, neural architectures, biological neural network models, etc., can solve problems such as poor scalability, poor scoring prediction accuracy, and sparseness, and achieve high prediction accuracy and scalability Strong, high predictive accuracy

Active Publication Date: 2019-05-10
XIDIAN UNIV
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Problems solved by technology

The disadvantage of this method is that this method only uses the global feature information between users and items, which leads to insufficient information mining between users and items, and poor scoring prediction accuracy.
The disadvantage of this m...

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  • An article score prediction method based on matrix decomposition and neural collaborative filtering
  • An article score prediction method based on matrix decomposition and neural collaborative filtering
  • An article score prediction method based on matrix decomposition and neural collaborative filtering

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Embodiment Construction

[0054] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0055] refer to figure 1 , the implementation steps of the present invention are further described in detail.

[0056] Step 1, construct a user-item rating matrix.

[0057] From the user-item scoring data set, extract the user ID and item ID corresponding to each rating to form the user's rating matrix for the item, where the behavior of the rating matrix is ​​the user ID, and the column of the rating matrix is ​​the item ID. The number of rows of the rating matrix is ​​the total number of users, and the number of columns of the rating matrix is ​​the total number of items.

[0058] 80% of the ratings randomly selected from the rating matrix form the training matrix, and the remaining 20% ​​of the ratings form the testing matrix.

[0059] In the embodiment of the present invention, the user's rating data set for the item includes a MovieLens-100K rating da...

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Abstract

The invention discloses an article score prediction method based on matrix decomposition and neural collaborative filtering. The method comprises the following steps: (1) constructing a user-; An article scoring matrix; (2) performing matrix decomposition on the training matrix; (3) constructing a neural collaborative filtering network; (4) performing neural collaborative filtering on the trainingmatrix; (5) extracting user and article characteristics of the neural collaborative filtering network embedded layer; (6) constructing a nearest neighbor feature matrix; (7) generating a scoring training set and a scoring test set; (8) training the fully connected neural network; and (9) carrying out score prediction on the score test set. The method has the advantages that the user and article information is fully mined, the article score prediction accuracy is high, and the expandability is high.

Description

technical field [0001] The invention belongs to the field of computer technology, and further relates to an item rating prediction method based on matrix decomposition and neural collaborative filtering in the technical field of item rating prediction. The present invention can use a model-based method to perform training according to the historical rating information of the user on the item, and obtain the rating prediction of the user on the item that has not been evaluated. Background technique [0002] The recommendation system is an information filtering system. By analyzing the user's historical behavior data and the characteristics of each user, it discovers the user's interests and hobbies, and recommends items of interest to the user. Nowadays, there are many recommendation methods that exist. Among them, collaborative filtering is the most widely used and successful recommendation method. Among them, the model-based recommendation algorithm can effectively solve ...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08G06F16/9535G06Q30/06
Inventor 慕彩红刘逸刘海艳吴建设李阳阳刘若辰熊涛
Owner XIDIAN UNIV
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