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Anonymous Microsoft Web Data Data Set
Download: Data Folder, Data Set Description

Abstract: Log of anonymous users of www.microsoft.com; predict areas of the web site a user visited based on data on other areas the user visited.

Data Set Characteristics:  

N/A

Number of Instances:

37711

Area:

Computer

Attribute Characteristics:

Categorical

Number of Attributes:

294

Date Donated

1998-11-01

Associated Tasks:

Recommender-Systems

Missing Values?

N/A

Number of Web Hits:

52453


Source:

Creators:

Jack S. Breese, David Heckerman, Carl M. Kadie
Microsoft Research, Redmond WA, 98052-6399, USA
breese '@' microsoft.com, heckerma '@' microsoft.com, carlk '@' microsoft.com

Donors:

Breese:, Heckerman, & Kadie


Data Set Information:

We created the data by sampling and processing the www.microsoft.com logs. The data records the use of www.microsoft.com by 38000 anonymous, randomly-selected users. For each user, the data lists all the areas of the web site (Vroots) that user visited in a one week timeframe.

Users are identified only by a sequential number, for example, User #14988, User #14989, etc. The file contains no personally identifiable information. The 294 Vroots are identified by their title (e.g. "NetShow for PowerPoint") and URL (e.g. "/stream"). The data comes from one week in February, 1998.


Attribute Information:

Each attribute is an area ("vroot") of the www.microsoft.com web site.

The datasets record which Vroots each user visited in a one-week timeframe in Feburary 1998.


Relevant Papers:

J. Breese, D. Heckerman., C. Kadie _Empirical Analysis of Predictive Algorithms for Collaborative Filtering_ Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, WI, July, 1998.
[Web Link]

Also, expanded as Microsoft Research Technical Report MSR-TR-98-12, The papers are available on-line at: [Web Link]


Papers That Cite This Data Set1:

W. Nick Street and Yoo-Hyon Kim. A streaming ensemble algorithm (SEA) for large-scale classification. KDD. 2001. [View Context].

Dmitry Pavlov and Darya Chudova and Padhraic Smyth. Towards scalable support vector machines using squashing. KDD. 2000. [View Context].

Dmitry Pavlov and Jianchang Mao and Byron Dom. Scaling-Up Support Vector Machines Using Boosting Algorithm. ICPR. 2000. [View Context].

Kristin P. Bennett and Erin J. Bredensteiner. Geometry in Learning. Department of Mathematical Sciences Rensselaer Polytechnic Institute. [View Context].


Citation Request:

Please refer to the Machine Learning Repository's citation policy


[1] Papers were automatically harvested and associated with this data set, in collaboration with Rexa.info

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