Control method for interest subject excavation based on time window

Abstract

The invention provides a control method for interest subject excavation based on a time window. The control method comprises the steps of: a. determining a user label record and a social label record; b. determining a standard label record according to the user label record and the social label record; c. generating a user interest subject tree according to the standard label record; d. building a window and determining the corresponding relation between the window and the subject tree; and e. calculating the weight of the subject tree according to the corresponding relation. The invention also provides a corresponding control device. According to the invention, a great deal of user comparison and calculation are not needed to be referred; the interest subject tree is built by utilizing a prior probability formula, thus only the label record of each item of the user is needed to be scanned; and in a synergic label system, the user label record has respective characteristics and can accurately reflect the user personality, so that the whole users have sociality on the premise of not losing personality.

Description

technical field [0001] The invention relates to the technical field of user behavior service recommendation, in particular to an algorithm for discovering user points of interest from user historical behavior to implement user recommendation. Background technique [0002] The Web has become an important way for people to obtain information. Due to the increasing amount of Web information, the so-called "information overload" problem makes people have to spend a lot of time searching and browsing the information they need. Search engines are the most common tools to assist people in retrieving information, such as Google, Baidu and so on. Information retrieval technology meets people's certain needs, but due to its general nature, it still cannot meet the query requests of different backgrounds, different purposes and different periods. Especially in the field of e-commerce such as Amazon, Taobao, etc., how to allow users to obtain the products they need conveniently and qui...

AI Technical Summary

Problems solved by technology

Collaborative filtering technology is based on users' display ratings on items. Its advantage is that it can filter concepts that are difficult to express. The disadvantage is that users need to actively display to rate items.

Content-based filtering mainly uses text processing technology to analyze the user's description information of the project. Its advantage is that it can recommend users more accurately. At the same time, its defect is that it requires relatively high requirements for text processing, usually By extracting keywords to build relevant models for analysis, the time and complexity of the time spent is relatively high

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