Personalized recommendation method and system based on probability model and user behavior analysis

Abstract

The invention discloses a personalized recommendation method and system based on a probability model and user behavior analysis. The method includes the steps that article information and article attribute information are extracted, and operation behaviors of users on articles are extracted; interest points are obtained according to the article attribute information and the operation behaviors of the users on the articles; user interest similarity is obtained according to the operation behaviors of the users on the articles, and similar users are obtained; a decay factor is obtained according to the operation behaviors of the users on the articles based on the time dimension, and a user model is set up; interest characteristic information, at all dimensions, of the users is obtained according to the user model; after filtering, a recommendation algorithm is adopted to generate results to be recommended, and algorithm fusion is conducted to obtain personalized recommendation results of the users. After original data is preprocessed, the user model is set up, the interest points of the users and essential information acquisition requirements are depicted accurately to provide accurate personalized recommendation, and therefore the problems of information overload and long-tail articles in the network are solved.

Description

technical field

[0001] The invention relates to the technical field of computer data processing, in particular to a personalized recommendation method and system based on probability models and user behavior analysis. Background technique

[0002] With the development of Internet information technology, especially the rise of e-commerce, Internet content has shown explosive growth, and people have gradually entered the era of massive data. Faced with a wide variety of products, movies, songs, videos and other services every day, but at a loss what to do, this is the problem of information overload that is often mentioned. At the same time, various items on the Internet have a long tail phenomenon, which means that most of the items are unpopular and have no chance to be displayed. Chris Anderson pointed out in the book "Long Tail Theory" published in 2006 that the traditional 80 / 20 principle (80% of sales come from 20% of popular brands) is facing more challenges in the Int...

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