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Automatically and proactively targeting the interested individuals by their surfing behavior

IP.com Disclosure Number: IPCOM000240901D
Publication Date: 2015-Mar-11
Document File: 5 page(s) / 97K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is a solution of getting users' preference/interest in a more proactive way by splitting the web content into several blocks (e.g. 4 blocks) in both verticil and horizontal directions and assign weight to each block. The block in the central of a web content has the highest weight and the ones in the most upper(or the most right) and most lower (or the most left) part has the least weight. Then we catch the stay time the users spend in each block. Each block will then have a score which is determined by multiplying stay time and weight of the block. The block that has the highest score is the ones that users are most interested in.

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Automatically and proactively targeting the interested individuals by their surfing behavior

Background:

Everyone knows the importance of content and how critical it is to delivering successful marketing campaigns and driving website traffic that can generate leads and increase revenue.

However, we are often fed by advertisement that don't meet my current preference, e.g. hotel information for the city that I visited in last month.

The reasons are due to the fact that current methodology in providing contents interesting to users are mostly based on what users had done.

For example:
1. the fans page that the user had selected
2. the websites the users had visited (cookies)
3. the profiles users register at beginning
4. live vote the users select

In most cases, the populated contents are based on users' historical actions and are very much likely an outdated information.

Can't we detect user's preference in a way that is more proactive but less rely on user's input?

Core idea:

Our solution is get users' preference/interest in a more proactive way by splitting the web content into several blocks (e.g. 4 blocks) in both verticil and horizontal directions and assign weight to each block.

The block in the central of a web content has the highest weight and the ones in the most upper(or the most right) and most lower (or the most left) part has the least weight.

Then we catch the stay time the users spend in each block. Each block will then have a score which is determined by multiplying stay time and weight of the block.

The block that has the highest score is the ones that users are most interested in.

By this solution, the content provider is able to pop up users' interest contents simultaneously but not rely on the historical information.

More over, users even don't have to provide any information but our solution will catch their preference in background.

Advantage:

1


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1. Detect user's preference without any input and actions from users.

2. Provide interest topics/contents to users in real time.

3. The data generated from our solution can be applied widely. It can be used in advertisement. The pop-up advertisement will be exactly what users are looking for as our solution catch what the users are interested in right now. It can also be applied in company's website. The most appropriate product will be presented to users based on user's interest and increase the consuming rate.

4. Correctly identify potential customers to increase business revenue.

5. Auto screen out disliked contents for users to increase user experience when surfing the subject website.

6. Avoid waste of internet resource by loading/distributing only the necessary and interesting contents to the target users.

Description:

Usage case 1: A real-time con...