News recommendation method and topic characterization method based on RNN and attention mechanism

A news and topic technology, applied in the field of news recommendation methods and topic representation, can solve problems such as difficulty in finding user interest migration, less consideration of sequential features, and topics that do not have much significance.

CN109492157AActive Publication Date: 2019-03-19HUAQIAO UNIVERSITY
6 Cites 42 Cited by

Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
Publication Date
2019-03-19

Smart Images

  • Figure 1
    Figure 1
  • Figure 2
    Figure 2
  • Figure 3
    Figure 3
Patent Text Reader

Abstract

The invention relates to an RNN and attention mechanism-based news recommendation method and a topic representation method, which combine a traditional topic model with a neural network word vector and can effectively improve the accuracy of semantic extraction and representation of news content texts. The RNN is used for describing the seriality characteristics of news browsing of the user, so that the timeliness of the personalized news recommendation content can be greatly improved; influence weights of different news on recommendation prediction are distinguished by utilizing an attentionmechanism, user interest migration can be captured, and the accuracy and novelty of personalized news recommendation contents are improved; and finally, in combination with an attention mechanism of aDBSCAN density clustering algorithm; performing heuristic discovery on the new topics and the old topics through density clustering, and dynamically calculating influence weights of news by utilizinga topic clustering result, so that the novelty of the recommended topics is improved.
Need to check novelty before this filing date? Find Prior Art

Description

technical field

[0001] The invention belongs to the technical field of data mining and neural network, and in particular relates to a news recommendation method and topic representation method based on RNN and attention mechanism. Background technique

[0002] With the development of information technology and the Internet, people have gradually entered the era of information overload from the era of information scarcity. As the main way for the public to obtain all kinds of information, various news websites are flooded with massive amounts of information every day. How to screen and filter news content, how to grasp the personalized needs of different users, how to follow up the migration of users’ reading interests, and how to provide users with novel, accurate and timely personalized recommendations have become the challenges faced by news websites. main challenge. Under the background of this demand, personalized news recommendation system came into being.

[0003] A...

Examples

Embodiment Construction

[0078] The technical solutions in the embodiments of the present invention will be described and discussed in detail below in conjunction with the drawings of the present invention. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0079] This embodiment uses crawler technology to crawl 111,257 news items browsed by 2,000 users of "Caixin.com" within one month as a news corpus; wherein, 2,875 news items in the last week of the month are used as a recommendation prediction data set, The remaining news serves as the training dataset for recommendations.

[0080] see Figure 1 to Figure 3 As shown, the embodiment of the present invention is a personalized news recommendation method, including: news grabbing step, data preprocessing step, word vector training step, topic model training step, topic representation vector calculation step, news deduplication step, RNN-based The ...