Identifying and Analyzing User Model Information from Collaborative Filtering Datasets

Josephine Griffith

Identifying and Analyzing User Model Information from Collaborative Filtering Datasets

Computer Science

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

Abstract

This paper considers the information that can be captured about users from a collaborative filtering dataset. The aims of the paper are to create a user model and to use this model to explain the performance of a collaborative filtering approach. A number of user features are defined and the performance of a collaborative filtering system in producing recommendations for users with different feature values is tested. Graph-based representations of the collaborative filtering space are presented and these are used to define some of the user features as well as being used in a recommendation task.

Original language	English (Ireland)
Title of host publication	PERSONALIZATION TECHNIQUES AND RECOMMENDER SYSTEMS
Publication status	Published - 1 Aug 2008

Authors (Note for portal: view the doc link for the full list of authors)

Authors
Griffith, J; O'Riordan, C; Sorensen, H

Cite this

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title = "Identifying and Analyzing User Model Information from Collaborative Filtering Datasets",

abstract = "This paper considers the information that can be captured about users from a collaborative filtering dataset. The aims of the paper are to create a user model and to use this model to explain the performance of a collaborative filtering approach. A number of user features are defined and the performance of a collaborative filtering system in producing recommendations for users with different feature values is tested. Graph-based representations of the collaborative filtering space are presented and these are used to define some of the user features as well as being used in a recommendation task.",

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year = "2008",

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AB - This paper considers the information that can be captured about users from a collaborative filtering dataset. The aims of the paper are to create a user model and to use this model to explain the performance of a collaborative filtering approach. A number of user features are defined and the performance of a collaborative filtering system in producing recommendations for users with different feature values is tested. Graph-based representations of the collaborative filtering space are presented and these are used to define some of the user features as well as being used in a recommendation task.

M3 - Conference Publication

BT - PERSONALIZATION TECHNIQUES AND RECOMMENDER SYSTEMS

ER -

Identifying and Analyzing User Model Information from Collaborative Filtering Datasets

Abstract

Authors (Note for portal: view the doc link for the full list of authors)

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Cite this