# US 2014/0156393 A1 · Method and system for recommending media information post

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- Number: US 2014/0156393 A1
- Document ID: US20140156393A1
- Status: Published application
- Kind: A1
- Application number: 14/117,568
- Filed: 2012-07-11
- Published: 2014-06-05
- Earliest claimed: 2011-09-09
- Assignees: Tencent Technology (Shenzhen) Company Limited
- Inventors: Liu Wu; Yu Huang
- Web page: https://patentgrep.com/patent/US20140156393A1

## CPC

- G06Q 30/0251: Targeted advertisements
- G06Q 30/0282: Rating or review of business operators or products
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR

## Abstract

Disclosed is a media information post recommendation method, including: calculating the recommendation index of a media information post according to the degree of matching between the industry to which a customer product belongs and a channel, as well as the target population covered by each media information post; and recommending a media information post to users according to the calculated recommendation index. Accordingly disclosed is a media information post recommendation system. The embodiments of the present disclosure do not rely on human experience to recommend media information posts, so it is possible to achieve systematic media information post recommendation and improve the recommendation efficiency of the media information posts as well as the releasing effect of media information.

## Claims

### Claim 1

A method for recommending a media information post, comprising:
calculating a recommendation index of a media information post according to a degree of matching between an industry to which a customer product belongs and a channel, as well as a target population covered by each media information post; and
recommending a media information post to users according to a calculated recommendation index.

### Claim 2 (depends on 1)

The method according to claim 1, wherein the degree of matching between the industry to which the customer product belongs and the channel is represented by a feature matching function, and the feature matching function is:
w
ij
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j
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N
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j
where fi,j represents the quantity of releasing times of a product of industry Ii on channel Lj, ΣLj represents the sum of carousels on channel Lj, N is the total number of industries, and nj is the quantity of industries to which products released on channel Lj belong.

### Claim 3 (depends on 1)

The method according to claim 1, wherein attributes of the target population covered by the media information post consist of age, gender, region and scenario.

### Claim 4 (depends on 1)

The method according to claim 1, wherein the calculating a recommendation index of a media information post comprises: calculating the recommendation index according to a recommendation index function R=W1×M+W2×L, where W1 and W2 are respectively the degree of matching between the channel to which the media information post belongs and the industry to which the customer product belongs, and a weight of the quantity of customer's target populations on the media information post, M is a ranking of the degree of matching between the channel to which the media information post belongs and the industry to which the customer product belongs, and L is a ranking of the quantity of the customer's target populations on the media information post.

### Claim 5 (depends on 1)

The method according to claim 1, wherein the recommending a media information post to users according to a calculated recommendation index comprises: presenting media information posts according to a descending order of recommendation indexes.

### Claim 6

A system for recommending a media information post, comprising: a recommendation index calculating unit and a media information post recommending unit,
wherein the recommendation index calculating unit is configured to calculate a recommendation index of a media information post according to a degree of matching between an industry to which a customer product belongs and a channel, as well as a target population covered by each media information post;
wherein the media information post recommending unit is configured to recommend a media information post to users according to a recommendation index calculated by the recommendation index calculating unit.

### Claim 7 (depends on 6)

The system according to claim 6, wherein the degree of matching between the industry to which the customer product belongs and the channel is represented by a feature matching function, and the feature matching function is:
w
ij
=
f
i
,
j
∑

L
j


log


N
n
j
where fi,j represents the quantity of releasing times of a product of industry Ii on channel Lj, ΣLj represents the sum of carousels on channel Lj, N is the total number of industries, and nj is the quantity of industries to which products released on channel Lj belong.

### Claim 8 (depends on 6)

The system according to claim 6, wherein attributes of the target population covered by the media information post consist of age, gender, region and scenario.

### Claim 9 (depends on 6)

The system according to claim 6, wherein the recommendation index calculating unit calculates a recommendation index of a media information post in a following manner: calculating the recommendation index according to a recommendation index function R=W1×M+W2×L, where W1 and W2 are respectively the degree of matching between the channel to which the media information post belongs and the industry to which the customer product belongs, and a weight of the quantity of customer's target populations on the media information post, M is a ranking of the degree of matching between the channel to which the media information post belongs and the industry to which the customer product belongs, and L is a ranking of the quantity of the customer's target populations on the media information post.

### Claim 10 (depends on 6)

The system according to claim 6, wherein the media information post recommending unit recommends the media information post to the users according to a calculated recommendation index in a following manner: presenting media information posts according to a descending order of recommendation indexes.

### Claim 11 (depends on 2)

The method according to claim 2, wherein the calculating a recommendation index of a media information post comprises: calculating the recommendation index according to a recommendation index function R=W1×M+W2×L, where W1 and W2 are respectively the degree of matching between the channel to which the media information post belongs and the industry to which the customer product belongs, and a weight of the quantity of customer's target populations on the media information post, M is a ranking of the degree of matching between the channel to which the media information post belongs and the industry to which the customer product belongs, and L is a ranking of the quantity of the customer's target populations on the media information post.

### Claim 12 (depends on 3)

The method according to claim 3 wherein the calculating a recommendation index of a media information post comprises: calculating the recommendation index according to a recommendation index function R=W1×M+W2×L, where W1 and W2 are respectively the degree of matching between the channel to which the media information post belongs and the industry to which the customer product belongs, and a weight of the quantity of customer's target populations on the media information post, M is a ranking of the degree of matching between the channel to which the media information post belongs and the industry to which the customer product belongs, and L is a ranking of the quantity of the customer's target populations on the media information post.

### Claim 13 (depends on 2)

The method according to claim 2, wherein the recommending a media information post to users according to a calculated recommendation index comprises: presenting media information posts according to a descending order of recommendation indexes.

### Claim 14 (depends on 3)

The method according to claim 3, wherein the recommending a media information post to users according to a calculated recommendation index comprises: presenting media information posts according to a descending order of recommendation indexes.

### Claim 15 (depends on 7)

The system according to claim 7, wherein the recommendation index calculating unit calculates a recommendation index of a media information post in a following manner: calculating the recommendation index according to a recommendation index function R=W1×M+W2×L, where W1 and W2 are respectively the degree of matching between the channel to which the media information post belongs and the industry to which the customer product belongs, and a weight of the quantity of customer's target populations on the media information post, M is a ranking of the degree of matching between the channel to which the media information post belongs and the industry to which the customer product belongs, and L is a ranking of the quantity of the customer's target populations on the media information post.

### Claim 16 (depends on 8)

The system according to claim 8, wherein the recommendation index calculating unit calculates a recommendation index of a media information post in a following manner: calculating the recommendation index according to a recommendation index function R=W1×M+W2×L, where W1 and W2 are respectively the degree of matching between the channel to which the media information post belongs and the industry to which the customer product belongs, and a weight of the quantity of customer's target populations on the media information post, M is a ranking of the degree of matching between the channel to which the media information post belongs and the industry to which the customer product belongs, and L is a ranking of the quantity of the customer's target populations on the media information post.

### Claim 17 (depends on 7)

The system according to claim 7, wherein the media information post recommending unit recommends the media information post to the users according to a calculated recommendation index in a following manner: presenting media information posts according to a descending order of recommendation indexes.

### Claim 18 (depends on 8)

The system according to claim 8, wherein the media information post recommending unit recommends the media information post to the users according to a calculated recommendation index in a following manner: presenting media information posts according to a descending order of recommendation indexes.