Method and method for recommending data

A data recommendation and data technology, applied in the direction of network data retrieval, network data indexing, electronic digital data processing, etc., can solve problems such as inability to consider changes in user interests, influence of user interest points, limitations, etc.

Inactive Publication Date: 2014-06-18
SHENGLE INFORMATION TECH SHANGHAI
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] 1. When users read data such as information, their purpose is not to discover information and acquire knowledge, but more often they will have such an attitude: "Give me something interesting." Therefore, if you pass every The information itself models the user's interest model, and then recommends information to the user according to the interest model, but the recommended information is often limited to the content that the user already knows;
[0005] 2. Users' points of interest are easily affected by surrounding emergencies. For example, many users are called "big game fans", that is, these users have never paid attention to football before, but due to the recent "European Cup" in full swing and media publicity Discuss with colleagues and friends around, so that these u

Method used

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Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0055] Such as figure 1 As shown, the present invention provides a data recommendation method, including:

[0056] Step S11, collecting and storing the user's historical records of data categories. Specifically, the data is various types of information. In this step, it is necessary to record the user’s reading behavior of the content in the data category, that is, to record the data such as information that the user has read at a certain moment. The log collection of user history records is a follow-up statistics and the basis for generating recommendation results.

[0057] Step S12, acquiring the user's conditional probability value for each data category according to the user's historical record of the data category.

[0058] Preferably, the conditional probability value is obtained by the following formula:

[0059] p ( click | C i ) = ...

Embodiment 2

[0076] Such as figure 2 As shown, the present invention provides another data recommendation method. The difference between this embodiment and Embodiment 1 is that the step of selecting data that meets the preset conditions from each data category with the highest preference value and recommending it to the user is added. Thereby making the recommendation result more optimized, the method includes:

[0077] Step S21, collecting and storing the user's historical records of data categories. Specifically, the data is various types of information. In this step, it is necessary to record the user’s reading behavior of the content in the data category, that is, to record the data such as information that the user has read at a certain moment. The log collection of user history records is a follow-up statistics and the basis for generating recommendation results.

[0078] Step S22, acquiring the user's conditional probability value for each data category according to the user's h...

Embodiment 3

[0098] Such as image 3 As shown, the present invention also provides another data recommendation system, including a history record module 1 , a conditional probability module 2 , a reading trend module 3 , a history preference module 4 and a recommendation module 5 .

[0099] The historical record module 1 is used to collect and store the user's historical records of data categories.

[0100] The conditional probability module 2 is used to obtain the user's conditional probability value for each data category according to the user's historical records for the data category.

[0101] Preferably, the conditional probability value is obtained by the following formula:

[0102] p ( click | C i ) = P ( C i | click ...

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Abstract

The invention relates to a method and system for recommending data, wherein the method comprises the steps of obtaining a conditional probability value of a user to each data category according to historical records of the user to data categories, obtaining a read trend value of all users to each data category according to the historical records of all the users to the data categories, obtaining a historical preference value of one user to each data category in a time period according to the historical records of the user to the data categories, obtaining a predicted preference value of the user to each data category in a time period according to the conditional probability value, the read trend value and the historical preference value, and ranking the predicted preference values in the same time period from high to low, obtaining first M data categories highest in the predicted preference value and recommending the obtained M data categories to the user. The method and the system are capable of determining the data categories in which the user are interested, and obtaining data from the data categories and recommending the data to the user, and as a result, the method and the system are capable of recommending interested contents for the user more effectively, reducing invalid browsing of the user and improving the read satisfaction of the user.

Description

technical field [0001] The invention relates to a data recommendation method and system. Background technique [0002] With the popularity of 3G networks and the rise of smart phones and tablet computers, users can access information anytime, anywhere. Surveys show that users usually have a strong need to browse, read information, and pass free time through smartphones and tablet computers. [0003] Existing data recommendation methods or systems usually have the following problems: [0004] 1. When users read data such as information, their purpose is not to discover information and acquire knowledge, but more often they will have such an attitude: "Give me something interesting." Therefore, if you pass every The information itself models the user's interest model, and then recommends information to the user according to the interest model, but the recommended information is often limited to the content that the user already knows; [0005] 2. Users' points of interest a...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/951
Inventor 姚璐陈运文纪达麒刘作涛辛颖伟王文广
Owner SHENGLE INFORMATION TECH SHANGHAI
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