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News recommendation system and method based on folfm model

A recommendation system, news technology, applied in special data processing applications, instruments, website content management, etc., can solve problems such as inability to meet real-time requirements

Active Publication Date: 2018-02-23
NANJING UNIV OF POSTS & TELECOMM
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, real-time performance is very important in the news recommendation system. The traditional collaborative filtering method needs to scan all user behavior records for each training, which cannot meet the real-time requirements.

Method used

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  • News recommendation system and method based on folfm model
  • News recommendation system and method based on folfm model
  • News recommendation system and method based on folfm model

Examples

Experimental program
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Embodiment Construction

[0095]Preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0096] The embodiment is a news recommendation system and method based on the FOLFM hidden semantic model, which relates to a hidden model optimization modeling for user implicit behavior data sets, through the training and calculation of the user's real-time behavior records. The preference of a certain hidden news is calculated to determine whether the news is recommended to the user, and the final news recommendation list is obtained through a series of cache storage and MongoDB database storage optimization and processing.

[0097] The main purpose of the design of the system is to abstract the news content model by using the latent model and content features on the basis of the content recommendation method, and construct a personalized latent preference model for each user. Through the real-time training of the user's real-time behavior ...

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Abstract

The invention provides a news recommendation system and method based on an FOLFM model. Based on a content recommendation method, a news content model is expressed abstractly through a latent class model and content characteristics, and an individual latent class preference model is built for each user. Real-time training is carried out on a real-time behavior record of a user to obtain preference, on certain latent class news, of the user, whether the news is recommended to the user is determined through calculation, and a final news recommendation list is obtained after a series of processing processes. The news recommendation system and method based on the FOLFM model deeply excavate the interest of the user, improve recommendation accuracy and satisfaction of the user, avoid a cold starting problem of the news, and guarantee performance under the condition that the recommendation effect is improved as much as possible. The experiment shows that the news recommendation system and method based on the FOLFM model not only guarantee the requirements for high accuracy and high speed, but also realize visual real-time recommendation for the user.

Description

technical field [0001] The present invention relates to a personalized news recommendation system and method, specifically a news recommendation method based on a FOLFM latent semantic model, which mainly improves the traditional LFM (Latent factor model, latent semantic model) into FOLFM (FastOnline Latent FactorModel, fast online learning latent factor model) Semantic model) and apply it to mine implicit feedback data sets in news websites, and make TopN recommendations through real-time news classification and user interest clustering, which belongs to the field of natural language processing. Background technique [0002] A personalized recommendation system is a tool that helps users quickly discover useful information, and can provide personalized services for different users to meet their specific interests and needs. Unlike search engines, recommendation systems do not require users to provide clear requirements, but instead model users' interests by analyzing their ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/30
CPCG06F16/9535G06F16/958
Inventor 张卫丰周磊王云王子元张迎周周国强
Owner NANJING UNIV OF POSTS & TELECOMM
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