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Commodity data retrieval sorting and commodity recommendation method and system

A technology of data retrieval and sorting method, applied in electronic digital data processing, special data processing applications, instruments, etc., can solve the problem of word frequency playing a decisive role, high degree, short document length, etc., to improve user experience, solve problems The effect of a lot of redundancy and few calculation steps

Active Publication Date: 2016-05-25
BEIJING GZT NETWORK TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

) degree is high, and the length of the document is short, word frequency does not play a decisive role in sorting
Although some methods can also be used to optimize the sorting, such as adjusting the weight of each document and the domain contained in the document, this approach is like a doctor who treats a person when he treats the disease but does not treat the disease. It cannot solve the fundamental problem.
For example, when a user searches for "mobile phone", the first thing displayed to the user may be books related to mobile phones, mobile phone chargers, etc. This is not in line with the user's search habits, and thus will greatly reduce the user experience.

Method used

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  • Commodity data retrieval sorting and commodity recommendation method and system
  • Commodity data retrieval sorting and commodity recommendation method and system
  • Commodity data retrieval sorting and commodity recommendation method and system

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Embodiment Construction

[0043] Specific embodiments of the present invention are given below, and the present invention is described in detail in conjunction with the accompanying drawings.

[0044] The category weight sorting method involved in the present invention can solve this problem well. Here, category refers to the category to which the product data belongs.

[0045] The basic idea is as follows:

[0046] Assume that the total number of hit results of a certain search is n (set n>0), and these n results belong to s (s>0) different hit categories, where the i-th (i=1, 2, ..., s) The number of hit results of each hit category is n i (n i >0), then there is In addition, set the number of all data contained in the i-th hit category in the original data as m i , obviously with m i ≥n i >0

[0047] Compute the category weight for each hit category. Let’s take the i-th (i=1, 2, ..., s) class, and its corresponding class weight is recorded as w i ,but:

[0048] w ...

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Abstract

The invention discloses a commodity data retrieval sequencing and commodity recommendation method which includes generating a data base of commodity data; taking out a matching result from the data base according to query words input by a user; counting the number of categories and all category names of the matching result and respectively calculating the number of commodity categories ni and the number of commodity data mi contained in the whole commodity data of each category in the matching result; calculating the category weight wi of each category and finding out the category weight wt corresponding to the largest category of the category weight wi; deleting all commodity data corresponding to the categories with category weights smaller than d*wt from the matching result, wherein 0<d<1 and the d is an adjustable parameter; and sequencing remaining categories from big to small according to the category weights of the categories, and displaying the percentage of the price of a commodity of a merchant and the selling price of the commodity in each online mart.

Description

technical field [0001] The invention relates to the field of computer data analysis, in particular to commodity data retrieval, sorting and commodity recommendation methods and support systems. Background technique [0002] The current ranking technology used by search engines is mainly the correlation ranking method, that is, the correlation between documents and query words is scored by establishing a model, and the documents with higher correlation scores are ranked higher. Different search engines use different correlation scoring models, mainly in the following categories: word frequency statistics, that is, the higher the frequency of query words contained in a document, the higher the relevance score of the document and the higher the ranking; hyperlinks Analysis method, that is, the more times a web page is linked and the more authoritative the linked site, the higher the quality of the web page. In addition, there is the click-through rate method, that is, the more...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/30
Inventor 郑茂林陈爱洁曾祥洪
Owner BEIJING GZT NETWORK TECH
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