Multidimensional-similarity-based personalized news recommendation method

A multi-dimensional similarity and similarity technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problems of data sparseness, inability to calculate, and low calculation accuracy, and achieve accuracy and recall rate improvement, accuracy The effect of improving the rate and recall rate and improving the quality of recommendation

Inactive Publication Date: 2015-04-22
北京格致璞科技有限公司 +1
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  • Abstract
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  • Application Information

AI Technical Summary

Benefits of technology

This patented recommender system uses two different methods for computing similarities with respect to both news sources (user activity) and news contents - one takes place beforehand while another considers how likely it's related to these things. By doing this, the recommendations made by the algorithm will be highly effective even if there may have fewer relevant items available due to lacking detailed knowledge about them. Additionally, the use of temporal features during top-n search helps reduce computational complexity compared to previous techniques like nearest neighbor searches. Overall, this technology enhances the relevance of individualized news suggestions over other media such as social networks and web pages.

Problems solved by technology

Technological Problem addressed in this patents relating to collaborive filtering recommender systems involves calculating similarity from both past and future versions of recommended objects instead of just one unitary weight basis. Current solutions involve simplifying the calculations involved, resulting in poorer performance compared to pure randomness techniques. Additionally, previous approaches either require prior knowledge about what people actually belong to an object being considered or rely heavily on parameter estimation algorithms.

Method used

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  • Multidimensional-similarity-based personalized news recommendation method
  • Multidimensional-similarity-based personalized news recommendation method
  • Multidimensional-similarity-based personalized news recommendation method

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Embodiment Construction

[0048] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0049] The present invention is a personalized news recommendation method that combines the user behavior similarity and news content similarity, and combines the multi-dimensional similarity of time features proposed for the particularity of the news field, and is used to improve the performance of the personalized news recommendation method Recommended quality.

[0050] As we all know, when recommending news personalizedly, since the news log system stores a large amount of implicit behavioral data (including browsing, commenting, posting, etc.) instead of explicit scoring data, how to effectively use these data to calculate the similarity of users or news is the first problem to be solved. The current similarity calculation method only uses user behavior data to

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Abstract

The invention discloses a multidimensional-similarity-based personalized news recommendation method. The method comprises the following steps of: extracting a set time record from a news log, capturing news and extracting a title and a text according to a news source address, performing word segmentation and noun extraction on the title and the text, and analyzing a noun sequence by using a subject model to acquire a subject feature character of the news; constructing a user model and a news model respectively according to the subject feature vector of the news and user behavior data; computing the content similarity and behavior similarity of users and the news respectively according to the user model, the news model and a time feature, computing final user similarity and final news similarity according to the content similarity and behavior similarity of the users and the news, and extracting a plurality of users and news which are most similar respectively; and generating user-based personalized recommendation results according to a latest news log record and a plurality of similar users which are most similar to a set user; or generating news-based personalized recommendation results according to the news on which the set user behaves and the news which is most similar to the news.

Description

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Claims

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Application Information

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Owner 北京格致璞科技有限公司
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