Associated information recommendation method and device

A technology for associating information and recommending methods, applied in the computer field, can solve the problems of content aggregation and inability to stimulate user interest points, etc.

Active Publication Date: 2015-02-18
BAIDU ONLINE NETWORK TECH (BEIJIBG) CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

This technology helps users find relevant items from their favorite sources more easily without having them pass over irrelevant ones like URLs on web pages that they are interested in learning about. It suggests assigning weights based on how many times an item has been accessed during its visitation process (clicked up) instead of just looking at what happens when someone else asks something later - this makes recommendation easier even with large amounts of unstructured data such as URL addresses.

Problems solved by technology

This patented technical solution described by this patents involves making recommendations based on searches or browsing requests while minimizing costs associated with advertising them over broadcast media such as TV shows. However, current methods only consider relevance between different types of data like titles, descriptions, ratings, hitings, etc., without considering how relevant these irrelevant features may affect people who want their suggestions more likely to engage with those products they already have.

Method used

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  • Associated information recommendation method and device
  • Associated information recommendation method and device
  • Associated information recommendation method and device

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0022] figure 1 A schematic flowchart of a method for recommending associated information provided in Embodiment 1 of the present invention; figure 1 As shown, the embodiment of the present invention includes the following steps:

[0023] Step 101, obtain m first weight values ​​corresponding to m second-level information nodes associated with the first-level information nodes, wherein the m first weight values ​​are obtained through the user's click and jump behavior, and the first-level The information node is the information node currently used by the user, and m is a positive integer;

[0024] Step 102: Determine recommended associated information for the user according to the m first weight values ​​and the preset n recommended information nodes, where n is a positive integer.

[0025] In the embodiment of the present invention, the first-level information node is A, and the m second-level information nodes are B1, B2, ..., Bm, and the association relationship among A, B1,

Embodiment 2

[0028] figure 2 It is a schematic flowchart of a method for recommending associated information provided in Embodiment 2 of the present invention, image 3 for figure 2 A schematic diagram of the directed graph of associated information in the illustrated embodiment; the embodiment of the present invention is illustrated by taking the number m of second-level information nodes greater than or equal to the number n of information nodes recommended to users as an example, as shown in figure 2 As shown, the embodiment of the present invention includes the following steps:

[0029] Step 201, acquiring the number of clicks and jumps of the user on the first-level information node, wherein the number of clicks and jumps includes a first number of jumps and a second number of jumps.

[0030] Step 202: Obtain m first weight values ​​of m second-level information nodes associated with the first-level information node according to the first jump count and the second jump count.

[00

Embodiment 3

[0046] Figure 4 It is a schematic flowchart of a method for recommending associated information provided in Embodiment 3 of the present invention, Figure 5 for Figure 4 A schematic diagram of the directed graph of associated information in the illustrated embodiment; the embodiment of the present invention is illustrated by taking the number m of second-level information nodes less than the number n of information nodes recommended to users as an example, as shown in Figure 4 As shown, the embodiment of the present invention includes the following steps:

[0047] Step 301, acquiring the number of clicks and jumps of the user on the first-level information node, wherein the number of clicks and jumps includes a first number of jumps and a second number of jumps.

[0048] Step 302: Obtain m first weight values ​​of m second-level information nodes associated with the first-level information node according to the first jump count and the second jump count.

[0049] Step 303,

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PUM

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Abstract

The invention provides an associated information recommendation method and device. The method comprises the steps of obtaining m first weight values corresponding to m second-level information nodes associated with a first-level information node, wherein the m first weight values are obtained through click jump behaviors of a user, the first-level information node is an information node which is used by the user at current, and the m is a positive integer; determining associated information to be recommended to the user according to the m first weight values and preset n recommended information nodes, wherein the n is a positive integer. The associated information recommendation method and device have the advantages that since the click jump behaviors of the user are referred through the m first weight values, the problem of content focusing for recommending node information to the user through the contents of information nodes, uploaders or writers of the information nodes, types of the information nodes and the like is avoided; since the m first weight values are obtained by referring to the click jump behaviors of the user, the effect of recommending associated information to users through a big data concept is realized.

Description

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Claims

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Application Information

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Owner BAIDU ONLINE NETWORK TECH (BEIJIBG) CO LTD
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