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Distance-based reverse k ranking query method

A query method and distance technology, applied in instruments, data processing applications, commerce, etc., can solve problems such as unpredictable k value, high algorithm complexity, and inability to find potential customers from non-popular merchants, so as to achieve the effect of ensuring real-time

Active Publication Date: 2018-04-13
EAST CHINA NORMAL UNIV
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AI Technical Summary

Problems solved by technology

The traditional personalized recommendation method based on machine learning requires long model training time and high algorithm complexity, which is difficult to adapt to the real-time requirements of the mobile environment
The existing distance-based reverse ranking query (reverse top-k query) can only find potential customers for some popular merchants, but for less popular merchants, the return result set of the query is often empty because the k value is unpredictable in advance , that is, unable to find suitable potential customers for non-popular merchants

Method used

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Embodiment

[0044] The present invention is named Algorithm 1 (ResponseRkranks). In order to make the implementation process more concise and clear, Algorithm 1 includes Algorithm 2 (FilterBucket).

[0045] figure 1 It is the specific processing flow of Algorithm 1. First, the merchant data set P, mobile user data set M, and mobile user preference data set W are input. Then, build indexes on each data set, including building a grid index on the location of the merchant and the location of the mobile user; build a KD tree index on the full-dimensional space of the merchant, and maintain a minimum circumscribing like the R tree at each non-leaf node Rectangular; build an equal-width histogram H on mobile user preferences.

[0046] After the corresponding index is established, the algorithm locates and queries the grid C where the merchant is located on the grid G, and uses Z to save the query return result, that is, the k users with the best ranking currently, in order to avoid repeated vi...

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Abstract

The invention discloses a distance-based reverse k ranking query method. According to the method, first, network indexes are used to perform indexing on current positions of a merchant and mobile users, a KD tree is used to perform indexing on all dimensions of the merchant, and an equi-width histogram is used to perform indexing on user preferences; and then based on the three indexes, a method based on lower bound pruning is used to quickly find k users who are most interested in the designated merchant. The method overcomes the natural defect in distance-based reverse ranking query, can return k potential clients to any merchant and is suitable for a mobile environment, and potential client orientation with the merchant being a center provides a solution to personalized precise marketing based on position services.

Description

technical field [0001] The invention belongs to the field of reverse ranking query in database technology, and in particular relates to a method for searching potential customers centered on merchants in a mobile environment. The distance-based reverse k-ranking query method proposed by the present invention mainly solves the problem of how to quickly return the k potential customers who are most interested in the merchant q given the query merchant q in the mobile environment. Background technique [0002] With the rapid development of wireless positioning technology and the popularization of various mobile devices, as well as the widespread use of Web 2.0 applications, merchants can easily collect user preferences, and can real-time according to the current location and preferences of mobile users. Some promotional information is pushed to some potential customers. The traditional personalized recommendation method based on machine learning has a long model training time ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06Q30/06
CPCG06Q30/0601G06F16/2228G06F16/2425G06F16/2453
Inventor 张召金澈清戚晓冬
Owner EAST CHINA NORMAL UNIV
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