The University of Southampton
University of Southampton Institutional Repository

A Scalable, Accurate Hybrid Recommender System

Ghazanfar, Mustansar and Prugel-Bennett, Adam (2010) A Scalable, Accurate Hybrid Recommender System At The 3rd International Conference on Knowledge Discovery and Data Mining (WKDD 2010), Thailand. 09 - 10 Jan 2010.

Record type: Conference or Workshop Item (Paper)


Recommender systems apply machine learning techniques for ?ltering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative ?lter- ing, content-based ?ltering, and demographic recommender systems. Collaborative ?ltering recommender systems rec- ommend 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 ?ltering 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 rec- ommendation 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.

PDF Scalable_accurate_HRS.PDF - Other
Download (140kB)

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


Local EPrints ID: 268430
PURE UUID: 441b0c22-b417-4ccb-8a70-451236b59a5c

Catalogue record

Date deposited: 28 Jan 2010 18:39
Last modified: 18 Jul 2017 06:54

Export record


Author: Mustansar Ghazanfar
Author: Adam Prugel-Bennett

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton:

ePrints Soton supports OAI 2.0 with a base URL of

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.