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Recommendation method and system based on linear regression

A linear regression and recommendation method technology, applied in the field of recommendation, can solve the problems that the results cannot be directly updated incrementally, the real-time performance of the application cannot be guaranteed, and the matrix decomposition takes time, etc., so as to improve the performance of the algorithm, eliminate the influence, and have good robustness Effect

Active Publication Date: 2017-06-30
TSINGHUA UNIV
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Problems solved by technology

However, due to the fact that the matrix decomposition itself is quite time-consuming, the real-time performance of the application cannot be guaranteed, and its results cannot be directly updated incrementally, which greatly limits its application in the industry.

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  • Recommendation method and system based on linear regression
  • Recommendation method and system based on linear regression
  • Recommendation method and system based on linear regression

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Embodiment Construction

[0038] The preferred embodiments will be described in detail below in conjunction with the accompanying drawings. It should be emphasized that the following description is only exemplary and not intended to limit the scope of the invention and its application.

[0039] The idea of ​​the present invention to solve the problem is: first, traverse all users and items in the current network system, and obtain the historical scoring data of all users and items; then, respectively establish a linear regression model based on users and a linear regression model based on items; then , according to the previously established linear regression model based on users and items, the highest frequency score in the historical ratings of users or items is used as the model input to predict the user's rating on the item; finally, sort according to the user's predicted rating of all unrated items , recommend items with higher rankings to users as candidates.

[0040] The specific implementation...

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Abstract

The invention discloses a recommendation method and system based on linear regression, and belongs to the technical field of recommendation. The recommendation method and system are used for solving the problem existing in the study of a current recommendation system. The method comprises the steps that traversal is carried out on all users and articles in a current network system, and historical score data of all the users and articles are obtained; a linear regression model based on the users is built according to the historical score data; a linear regression model based on the articles is built according to the historical score data; the scores of the articles which are not scored by the users are predicated through the linear regression model of the users and the linear regression model of the articles; the predicated scores of all the articles which are not scored by the users are ranked, and the highly-ranked articles are recommended to the users as candidates. The method and system based on the linear regression overcome the defect that a traditional collaborative filtering algorithm is poor in real-time performance and cannot directly carry out incremental updating in the practical application, and are effectively achieved.

Description

technical field [0001] The invention relates to the technical field of recommendation, in particular to a linear regression-based recommendation method and system. Background technique [0002] With the rapid development of Internet technology, big data has come. The development of social networks, e-commerce and mobile communications has enabled people to get rid of the situation of information scarcity and enter the era of massive data in petabytes (PateByte, PB). Sina Weibo has more than 60 million daily active users, and the average daily number of Weibo posts has increased to 130 million; Baidu’s daily query processing exceeds one billion; Taobao’s “Double Eleven” single-day transaction volume is as high as 170 million . With the explosive growth of data, problems also arise: how to mine the most valuable information for itself from the huge amount of data, and achieve the best match between information and users? This is a severe challenge for both information consu...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/30
CPCG06F16/951
Inventor 陈震谢峰冯喜伟尚家兴曹军威
Owner TSINGHUA UNIV
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