# US 10,503,710 B2 · Webpage pre-reading method, apparatus and smart terminal

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- Number: US 10,503,710 B2
- Document ID: US10503710B2
- Status: Granted
- Kind: B2
- Application number: 15/210,979
- Filed: 2016-07-15
- Published: 2017-01-26
- Granted: 2019-12-10
- Earliest claimed: 2015-07-20
- Assignees: Guangzhou Ucweb Computer Technology Co., Ltd.
- Applicants: Guangzhou Ucweb Computer Technology Co., Ltd
- Inventors: Jie Liang; Yuxin Jiang; Wenqing Yao; Huocheng Wu; Yanwei Xu
- Web page: https://patentgrep.com/patent/US10503710B2

## CPC

- G06F 16/215: Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
- G06F 16/24: Querying
- G06F 16/24578: {using ranking}
- G06F 16/9574: {of access to content, e.g. by caching}
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

A method is provided for webpage pre-reading. The method includes obtaining visiting information during a period of time for multiple webpages of each user from a plurality of users including a current user; determining respective similarity values between the current user and the plurality of users based on the visiting information; determining one or more most-similar users based on the similarity values; and when the current user is browsing a current webpage, predicting a target webpage to be accessed by the most-similar users as a target webpage for the current user and pre-reading the target webpage.

## Claims

### Claim 1

A method for webpage pre-reading, comprising:
obtaining visiting information during a period of time for multiple webpages of each user from a plurality of users including a current user;
determining a respective similarity values between the current user and each of the plurality of users based on the visiting information;
determining one or more most-similar users based on the respective similarity value;
determining a recommendation probability of a clicking path based on each of the one or more most-similar users, by calculating a product of the respective similarity value of each of the one or more most-similar users and a number of clicks from a first webpage to a second webpage and, wherein the first webpage and the second webpage forms the clicking path; and
when the current user is browsing a current webpage, predicting a target webpage to be accessed by the one or more most-similar users, based on the recommendation probability, as a target webpage for the current user and pre-reading the target webpage.

### Claim 2 (depends on 1)

The method of claim 1, further comprising:
when obtaining the visiting information during the period of time for the multiple webpages of each user from the plurality of users including the current user, performing a data cleaning process to screen the visiting information of each webpage.

### Claim 3 (depends on 1)

The method of claim 1, wherein determining the respective similarity value further includes:
calculating the respective similarity value between the current user and each of the plurality of users using a cosine similarity calculation formula.

### Claim 4 (depends on 3)

The method of claim 3, further comprising:
before calculating the similarity value, filtering users having a webpage visiting number that is less than a webpage visiting threshold number from the plurality of users,
wherein the webpage visiting number is determined as a lower limit of a confidence interval calculated by using Wilson interval formula.

### Claim 5 (depends on 1)

The method of claim 1, further comprising:
generating an inverted list using a same webpage visited by multiple users as a common characteristic; and
determining a similarity value between the current user and each of the plurality of users in the inverted list using a cosine similarity calculation formula.

### Claim 6 (depends on 5)

The method of claim 5, further comprising:
when a total number of users included in any inverted list exceeds a user threshold value, removing the exceeding inverted list.

### Claim 7 (depends on 1)

The method of claim 1, wherein predicting the target webpage further includes:
calculating recommendation probabilities of a plurality of clicking paths based on the one or more most-similar users, wherein each of the plurality of clicking paths is from the current webpage;
generating a pre-reading list including a subset of the plurality of clicking paths according to the recommendation probabilities of the plurality of clicking paths; and
searching the pre-reading list based on the current webpage to determine the target webpage to be accessed.

### Claim 8 (depends on 7)

The method of claim 7, wherein calculating the recommendation probabilities further includes:
obtaining a number of clicks by a most-similar user of the one or more most-similar users from the first webpage to the second webpage, the first webpage and the second webpage forming the clicking path; and
calculating a product of the number of clicks and a similarity value between the most-similar user and the current user as a recommendation probability of the clicking path.

### Claim 9 (depends on 8)

The method of claim 8, further comprising:
when multiple most-similar users click from the first webpage to the second webpage, calculating a sum of the recommendation probability of the click path of the multiple most-similar users as the recommendation probability of the clicking path.

### Claim 10 (depends on 8)

The method of claim 8, further comprising:
filtering any clicking path having a number of clicks less than a clicking number threshold.

### Claim 11 (depends on 7)

The method of claim 7, further comprising:
ranking the subset of the plurality of clicking paths in the pre-reading list in a descending order based on the recommendation probabilities of the plurality of clicking paths,
wherein pre-reading the target webpage further includes:
pre-reading a target webpage corresponding to a clicking path having a largest recommendation probability; or
pre-reading multiple target webpages corresponding to multiple clicking paths ranked in order based on their recommendation probabilities.

### Claim 12 (depends on 7)

The method of claim 7, further comprising:
filtering any clicking path having a recommendation probability less than a recommendation probability threshold.

### Claim 13

An apparatus for webpage pre-reading, comprising:
an information obtaining module configured for obtaining visiting information during a period of time for multiple webpages of each user from a plurality of users including a current user;
a determination module configured for determining a respective similarity value between the current user and each of the plurality of users based on the visiting information, and determining one or more most-similar users based on the respective similarity value;
a calculating and generating module configured for determining a recommendation probability of a clicking path based on each of the one or more most-similar users, by calculating a product of the respective similarity value of each of the one or more most-similar users and a number of clicks from a first webpage to a second webpage and, wherein the first webpage and the second webpage forms the clicking path;
an analysis module configured for, when the current user is browsing a current webpage, predicting a target webpage to be accessed by the one or more most-similar users as a target webpage for the current user; and
a first pre-reading module configured for pre-reading the target webpage.

### Claim 14 (depends on 13)

The apparatus of claim 13, wherein the information obtaining module includes:
a preprocessing module configured for performing a data cleaning process to screen the visiting information of each webpage.

### Claim 15 (depends on 13)

The apparatus of claim 13, wherein the determination module includes:
a filter module configured for filtering a user corresponding to a webpage visiting number that is less than a webpage visiting threshold number; and
a similarity calculation module configured for using a cosine similarity calculation formula to calculate the similarity value between the current user and each of the plurality of users.

### Claim 16 (depends on 13)

The apparatus of claim 13, wherein the determination module further includes:
an inverted list generating module configured for generating an inverted list including a subset of the plurality of users that have visited a same webpage visited by the current user; and
a most-similar user determination module configured for determining a similarity value between the current user and each user in the inverted list.

### Claim 17 (depends on 13)

The apparatus of claim 13, wherein the calculating and generating module is further configured for:
calculating recommendation probabilities of a plurality of clicking paths based on the one or more most-similar users, wherein each clicking path is from the current webpage; and
generating a pre-reading list including a subset of the plurality of clicking paths according to the recommendation probabilities of the plurality of clicking paths.

### Claim 18 (depends on 17)

The apparatus of claim 17, wherein the calculating and generating module is further configured for:
obtaining a number of clicks by a most-similar user of the one or more most-similar users from the first webpage to the second webpage, the first webpage and the second webpage forming the clicking path;
calculating a product of the number of clicks and a similarity value between the most-similar user and the current user as a recommendation probability of the clicking path; and
when multiple most-similar users click from the first webpage to the second webpage, calculating a sum of the recommendation probability of the click path of the multiple most-similar users as the recommendation probability of the clicking path.

### Claim 19 (depends on 17)

The apparatus of claim 17, further comprising a second pre-reading module configured for:
searching the pre-reading list based on the current webpage to determine the target webpage to be accessed; and
pre-reading data of the target webpage to be accessed.

### Claim 20 (depends on 19)

The apparatus of claim 19, the second pre-reading module being further configured for:
ranking the subset of the plurality of clicking paths in the pre-reading list in a descending order based on the recommendation probabilities of the plurality of clicking paths,
wherein pre-reading the target webpage further includes:
pre-reading a target webpage corresponding to a clicking path having a largest recommendation probability; or
pre-reading multiple target webpages corresponding to multiple clicking paths ranked in order based on their recommendation probabilities.