Recommendation method capable of aiming at classification information with items

A recommendation method, a technology for classifying information, applied in the field of recommendation systems, and can solve problems such as loss of universality of recommendation methods

Active Publication Date: 2016-09-21
GUILIN UNIV OF ELECTRONIC TECH
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  • Abstract
  • Description
  • Claims
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Problems solved by technology

Most of the recommendation methods are aimed at specific application

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  • Recommendation method capable of aiming at classification information with items
  • Recommendation method capable of aiming at classification information with items
  • Recommendation method capable of aiming at classification information with items

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Embodiment Construction

[0027] The principles and features of the present invention are described below in conjunction with the accompanying drawings, and the examples given are only used to explain the present invention, and are not intended to limit the scope of the present invention.

[0028] An example of the implementation of the present invention is given using the real dataset of MovieLens 1M (http: / / grouplens.org / datasets / movielens / ) in the recommendation field. This dataset contains 1,000,209 ratings of 3,900 movies by 6,400 independent anonymous users in 2000 Score, the value of the score is a discrete value between [1-5], and there are 18 types of labels. Movies are labeled with different classification labels, and each movie corresponds to one or more classification labels.

[0029] Utilize the method introduced in the summary of the invention to construct user category preference similarity matrix S (ucp) , and then use the joint matrix factorization to analyze the user rating matrix R a...

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Abstract

The invention discloses a recommendation method capable of aiming at classification information with items. In various network applications, users always need to be recommended, context information needs to be used for improving recommendation accuracy and enhancing user experience, but traditional context perception recommendation methods still face the challenge of a data sparsity problem. In order to further alleviate the data sparsity problem, the invention puts forward a novel recommendation method, which combines user rating data with user category preference to carry out article recommendation so as to solve the problem of low rating prediction accuracy when the user rating data is sparse. The method is suitable for large-scale data. An experiment result indicates that the method has a good recommendation effect when the method is compared with a traditional mainstream method.

Description

technical field [0001] The invention belongs to the field of recommendation systems and relates to a recommendation method for an application with an item classification function. Background technique [0002] The existing context-based recommendation systems are all recommendation methods that directly use the user's historical data. Although it is convenient, easy to use widely, and easy to get a wide range of evaluation benefits, but because the user's historical behavior data is usually very sparse , so these methods all face serious data sparsity problems. It is difficult to model user preferences based on sparse historical user behavior data, resulting in low accuracy of the recommendation system and affecting user experience. [0003] We want to recommend items for an application system. Generally, we need to analyze the composition of the recommendation system. Here we will discuss some of the main bodies that make up the recommendation system. The following is a b...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/337
Inventor 王勇何海洋刘永宏杜诚张文辉唐红武
Owner GUILIN UNIV OF ELECTRONIC TECH
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