Personalised information gathering and recommender systems: techniques and trends

Tao, Xiaohui and Zhou, Xujuan and Lau, Cher Han and Li, Yuefeng (2013) Personalised information gathering and recommender systems: techniques and trends. ICST Transactions on Scalable Information Systems, 13 (1-3). pp. 1-17. ISSN 2032-9407

[img] Text (Published Version)
Tao_Zhou_Lau_Li_ICST_TSIS_2013_PV.pdf
Available under License Creative Commons Attribution.

Download (231Kb)

Abstract

With the explosive growth of resources available through the Internet, information mismatching and overload have become a severe concern to users. Web users are commonly overwhelmed by huge volume of information and are faced with the challenge of finding the most relevant and reliable information in a timely manner. Personalised information gathering and recommender systems represent state-of-the-art tools for efficient selection of the most relevant and reliable information resources, and the interest in such systems has increased dramatically over the last few years. However, web personalization has not yet been well-exploited; difficulties arise while selecting resources through recommender systems from a technological and social perspective. Aiming to promote high quality research in order to overcome these challenges, this paper provides a comprehensive survey on the recent work and achievements in the areas of personalised web information gathering and recommender systems. The report covers concept-based techniques exploited in personalised information gathering and recommender systems.


Statistics for USQ ePrint 22941
Statistics for this ePrint Item
Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Copyright © 2013 Tao et al., licensed to ICST. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited. doi:10.4108/trans.sis.2013.01-03.e4
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 12 Apr 2013 07:07
Last Modified: 08 Feb 2018 06:57
Uncontrolled Keywords: personalisation, information gathering, recommender systems
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080107 Natural Language Processing
08 Information and Computing Sciences > 0807 Library and Information Studies > 080704 Information Retrieval and Web Search
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.4108/trans.sis.2013.01-03.e4
URI: http://eprints.usq.edu.au/id/eprint/22941

Actions (login required)

View Item Archive Repository Staff Only