Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Generating semantically enriched user profiles for web personalization

Tools
- Tools
+ Tools

Anand, Sarabjot Singh, Kearney, Patricia and Shapcott, Mary (2007) Generating semantically enriched user profiles for web personalization. ACM Transactions on Internet Technology, Volume 7 (Number 4). Article number 22. doi:10.1145/1278366.1278371 ISSN 1533-5399.

Research output not available from this repository.

Request-a-Copy directly from author or use local Library Get it For Me service.

Official URL: http://dx.doi.org/10.1145/1278366.1278371

Request Changes to record.

Abstract

Traditional collaborative filtering generates recommendations for the active user based solely on ratings of items by other users. However, most businesses today have item ontologies that provide a useful source of content descriptors that can be used to enhance the quality of recommendations generated. In this article, we present a novel approach to integrating user rating vectors with an item ontology to generate recommendations. The approach is novel in measuring similarity between users in that it first derives factors, referred to as impacts, driving the observed user behavior and then uses these factors within the similarity computation. In doing so, a more comprehensive user model is learned that is sensitive to the context of the user visit.

An evaluation of our recommendation algorithm was carried out using data from an online retailer of movies with over 94,000 movies, 44,000 actors, and 10,000 directors within the item knowledge base. The evaluation showed a statistically significant improvement in the prediction accuracy over traditional collaborative filtering. Additionally, the algorithm was shown to generate recommendations for visitors that belong to sparse sections of the user space, areas where traditional collaborative filtering would generally fail to generate accurate recommendations.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Journal or Publication Title: ACM Transactions on Internet Technology
Publisher: Association for Computing Machinery, Inc.
ISSN: 1533-5399
Official Date: October 2007
Dates:
DateEvent
October 2007Published
May 2007Accepted
November 2005Submitted
Volume: Volume 7
Number: Number 4
Number of Pages: 26
Article Number: Article number 22
DOI: 10.1145/1278366.1278371
Status: Peer Reviewed
Publication Status: Published

Data sourced from Thomson Reuters' Web of Knowledge

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item
twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us