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A Scalable, Accurate Hybrid Recommender System

A Scalable, Accurate Hybrid Recommender System
A Scalable, Accurate Hybrid Recommender System
Recommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative filtering, content-based filtering, and demographic recommender systems. Collaborative filtering recommender systems recommend items by taking into account the taste (in terms of preferences of items) of users, under the assumption that users will be interested in items that users similar to them have rated highly. Content-based filtering recommender systems recommend items based on the textual information of an item, under the assumption that users will like similar items to the ones they liked before. Demographic recommender systems categorize users or items based on their personal attribute and make recommendation based on demographic categorizations. These systems suffer from scalability, data sparsity, and cold-start problems resulting in poor quality recommendations and reduced coverage. In this paper, we propose a unique cascading hybrid recommendation approach by combining the rating, feature, and demographic information about items. We empirically show that our approach outperforms the state of the art recommender system algorithms, and eliminates recorded problems with recommender systems.
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 (2010) A Scalable, Accurate Hybrid Recommender System. The 3rd International Conference on Knowledge Discovery and Data Mining (WKDD 2010), Thailand. 09 - 10 Jan 2010.

Record type: Conference or Workshop Item (Paper)

Abstract

Recommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative filtering, content-based filtering, and demographic recommender systems. Collaborative filtering recommender systems recommend items by taking into account the taste (in terms of preferences of items) of users, under the assumption that users will be interested in items that users similar to them have rated highly. Content-based filtering recommender systems recommend items based on the textual information of an item, under the assumption that users will like similar items to the ones they liked before. Demographic recommender systems categorize users or items based on their personal attribute and make recommendation based on demographic categorizations. These systems suffer from scalability, data sparsity, and cold-start problems resulting in poor quality recommendations and reduced coverage. In this paper, we propose a unique cascading hybrid recommendation approach by combining the rating, feature, and demographic information about items. We empirically show that our approach outperforms the state of the art recommender system algorithms, and eliminates recorded problems with recommender systems.

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More information

Published date: 9 January 2010
Additional Information: Event Dates: 9-10 Jan 2010
Venue - Dates: The 3rd International Conference on Knowledge Discovery and Data Mining (WKDD 2010), Thailand, 2010-01-09 - 2010-01-10
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 268430
URI: http://eprints.soton.ac.uk/id/eprint/268430
PURE UUID: 441b0c22-b417-4ccb-8a70-451236b59a5c

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Date deposited: 28 Jan 2010 18:39
Last modified: 02 Dec 2019 21:01

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Contributors

Author: Mustansar Ghazanfar
Author: Adam Prugel-Bennett

University divisions

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