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Fulfilling the needs of gray-sheep users in recommender systems, a clustering solution

Fulfilling the needs of gray-sheep users in recommender systems, a clustering solution
Fulfilling the needs of gray-sheep users in recommender systems, a clustering solution
Recommender systems apply data mining techniques for filtering unseen information and can predict whether a user would like a given item. This paper focuses on graysheep users problem responsible for the increased error rate in collaborative filtering based recommender systems algorithms. The main contribution of this paper lies in showing that (1) the presence of gray-sheep users can affect the performance— accuracy and coverage—of collaborative filtering based algorithms, depending on the data sparsity and distribution; (2) graysheep users can be identified using clustering algorithms in offline fashion, where the similarity threshold to isolate these users from the rest of clusters can be found empirically; (3) contentbased profile of gray-sheep users can be used for making accurate recommendations. The effectiveness of the proposed algorithm is tested on the MovieLens dataset and community of movie fans in the FilmTrust Website, using mean absolute error, receiver operating characteristic sensitivity, and coverage.
Ghazanfar, Mustansar
9452876d-46e5-4c7d-9120-c486f448632d
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Ghazanfar, Mustansar
9452876d-46e5-4c7d-9120-c486f448632d
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e

Ghazanfar, Mustansar and Prugel-Bennett, Adam (2011) Fulfilling the needs of gray-sheep users in recommender systems, a clustering solution. 2011 International Conference on Information Systems and Computational Intelligence, Harbin, China. 18 - 20 Jan 2011. (Submitted)

Record type: Conference or Workshop Item (Paper)

Abstract

Recommender systems apply data mining techniques for filtering unseen information and can predict whether a user would like a given item. This paper focuses on graysheep users problem responsible for the increased error rate in collaborative filtering based recommender systems algorithms. The main contribution of this paper lies in showing that (1) the presence of gray-sheep users can affect the performance— accuracy and coverage—of collaborative filtering based algorithms, depending on the data sparsity and distribution; (2) graysheep users can be identified using clustering algorithms in offline fashion, where the similarity threshold to isolate these users from the rest of clusters can be found empirically; (3) contentbased profile of gray-sheep users can be used for making accurate recommendations. The effectiveness of the proposed algorithm is tested on the MovieLens dataset and community of movie fans in the FilmTrust Website, using mean absolute error, receiver operating characteristic sensitivity, and coverage.

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More information

Submitted date: 21 January 2011
Additional Information: Event Dates: 18-20, January
Venue - Dates: 2011 International Conference on Information Systems and Computational Intelligence, Harbin, China, 2011-01-18 - 2011-01-20
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 271770
URI: http://eprints.soton.ac.uk/id/eprint/271770
PURE UUID: 6b2de4e3-d303-45a0-b168-82b6049f8e01

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Date deposited: 11 Dec 2010 14:29
Last modified: 28 Sep 2020 16:33

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Contributors

Author: Mustansar Ghazanfar
Author: Adam Prugel-Bennett

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