Browse Prior Art Database

Method and System of Personal Information Demands Tracking on the Web

IP.com Disclosure Number: IPCOM000198087D
Publication Date: 2010-Jul-26
Document File: 1 page(s) / 79K

Publishing Venue

The IP.com Prior Art Database

Abstract

In the past years, the rapid development of online information has aroused a lot of studies from the industries and businesses, mainly due to its tremendous value for potential knowledge exploration. Communications between people within online communities is one of the most important information exploited from web 2.0 and represent a new path towards providing continuous information about user personal information demands status. Traditional approaches for user demand exploration on web are usually static without considering time factor. In fact, user’s demands on information change dynamically, especially along with their discussions with other people. The influence existing between users is obvious to change user’s idea and knowledge. It is necessary to detect such kind of change when exploring user’s demands on specific product or something else. A lot of potential applications can be implemented based on such kind of information discovery, such as, setup user personal profile for web advertising and product recommendation etc.

This text was extracted from a PDF file.
This is the abbreviated version, containing approximately 63% of the total text.

Page 1 of 1

Method and System of Personal Information Demands Tracking on the Web

3.1 Overall Architecture


The overall architecture is displayed in the Figure 1.

 User explicit requirements on product features

 User explicit requirements on product features

 User implicit requirements on product features

3.2 The personal information demands tracking on the web method mainly consists of the following steps:

Step 1: Detect information demander and the demanded objects.

   Step 1.1: based on QA detection techniques, posts with implicit or explicit information requirements are extracted.

Step 1.2:

NER and NLP techniques are implemented to identify who issue the request and for

Step 2: Reply-to relationship identification. By analyzing the structure of the focused web page, identify the responses relationship between users.

Step 3: User requirement detection.

Step 3.1: explicit requirement detection

   
Step 3.1.1: identify object features explicated discussed by user. It is based on the exploration of features from product review or specifications.

      Step 3.1.2: identify the specific requirement on each identified feature. Here, time factor are considered in case the change of user's requirement on the same feature. The final status of user requirement on each object feature is determined after the analysis throughout all the discussion thread.

Step 3.2: implicit requirement detection

      Step 3.2.1: collect user related information as much as possible, such as, related event of the targeted ob...