Syskill and Webert Web Page Ratings
Abstract
This database contains the HTML source of web pages plus the ratings of a single user on these web pages. The web pages are on four seperate subjects (Bands- recording artists; Goats; Sheep; and BioMedical) 数据挖掘研究院
Information files: 数据挖掘研究院
- SyskillWebert.data.html. Data description
- SyskillWebert.task.html. Classification task
Data files: 数据挖掘实验室
- SyskillWebert.tar.gz. (476K)
Syskill and Webert Web Page Ratings
Abstract
This database contains the HTML source of web pages plus the ratings of a single user on these web pages. The web pages are on four seperate subjects (Bands- recording artists; Goats; Sheep; and BioMedical) 数据挖掘研究院
Data Type
html web pages with user ratings 数据挖掘研究院
Sources
Original Owner and Donor
Michael Pazzani Department of Information and Computer Science, University of California, Irvine Irvine, CA 92697-3425 pazzani@ics.uci.edu 数据挖掘实验室
Date Donated: October 20, 1998 数据挖掘实验室
Data Characteristics
The HTML source of a web page is given. Users looked at each web page and inidated on a 3 point scale (hot medium cold) 50-100 pages per domain. However, this is realistic because we want to learn user profiles from as few examples as possible so that users have an incentitive to rate pages. 数据挖掘实验室
Data Format
Each subject is in a seperate directory. Within each directory, there is an file named "index". The index contains information on the other files. Each entry is a line of the form: 数据挖掘研究院
file-name | rating | url | date-rated | title
where file-name is the name of a file (usually an integer), rating is hot, medium, or cold. There are so few medium′s that mediums are usually merged with cold in experiments. 数据挖掘研究院
The other fields aren′t used in learning, but they are collected by the interface for other purposes. They are the url of the html source, the date rated and the title of the web oage. 数据挖掘研究院
Past Usage
Pazzani M., Billsus, D. (1997). Learning and Revising User Profiles: The identification of interesting web sites. Machine Learning 27, 313-331 数据挖掘研究院
Pazzani, M., Muramatsu J., Billsus, D. (1996). Syskill & Webert: Identifying interesting web sites. Proceedings of the National Conference on Artificial Intelligence, Portland, OR. PDF
Syskill and Webert Web Page Ratings
Task Type
classification 数据挖掘研究院
Sources
Donor
Michael Pazzani Department of Information and Computer Science, University of California, Irvine Irvine, CA 92697-3425 pazzani@ics.uci.edu 数据挖掘研究院
Date Donated: October 20, 1998 数据挖掘研究院
Problem Description
The problem is to predict user ratings for web pages (within a subject category). The HTML source of a web page is given. Users looked at each web page and inidated on a 3 point scale (hot medium cold) 50-100 pages per domain. However, this is realistic because we want to learn user profiles from as few examples as possible so that users have an incentitive to rate pages.
The accuracy of predicting ratings is reported in early publications. Later publications used the precision at top N or the F-measure. 数据挖掘研究院
Results
The initially study compared traditional meachine learning methods with IR methods. A variety of learning algorithms worked acceptably, including naive bayes, nearest neighbor, and Rocchio′s method
Pazzani M., Billsus, D. (1997). Learning and Revising User Profiles: The identification of interesting web sites. Machine Learning 27, 313-331 数据挖掘研究院
Pazzani, M., Muramatsu J., Billsus, D. (1996). Syskill & Webert: Identifying interesting web sites. Proceedings of the National Conference on Artificial Intelligence, Portland, OR. PDF
