demonstration

Context-aware intelligent recommender system

Author:

Mehdi ElahiAuthors Info & Claims

IUI '10: Proceedings of the 15th international conference on Intelligent user interfaces

Pages 407 - 408

https://doi.org/10.1145/1719970.1720045

Published: 07 February 2010 Publication History

Get Access

Abstract

This demo paper presents a context-aware recommendation system. The system mines data from user's web searches and other sources to improve the presentation of content on visited web pages. While user is browsing the internet, a memory resident agent records and analyzes the content of the webpages that were either searched for or visited in order to identify topic preferences. Then, based on such information, the content of requested web page is ranked and classified with different styles. The demo shows how a music weblog can be modified automatically based on user's affinities.

References

[1]

Official Google Blog: we knew the web was big..., access date: 2009/11/20, http://googleblog.blogspot.com/2008/07/we-knew-web-was-big.html.

Google Scholar

[2]

Orman, L., (1984). Fighting Information Pollution with Decision Support Systems. Journal of Management Information Systems, 1(2), pp. 64--71

Crossref

Google Scholar

[3]

Jändel, M. and Elahi, M. (2009). "Tribal Taste: Mobile Multiagent Recommender System", To Appear in the Proceedings of the International Conference on Intelligent User Interfaces 8-11 Feb., Sanibel Island, Florida, USA.

Digital Library

Google Scholar

[4]

Cornelius, P.(2007), "Neural Networks", Based on original lecture notes by Nedelko Grbic, 1999, Department of Signal Processing, Blekinge institute of Technology, Sweden

Google Scholar

[5]

Elahi, M. (2009). "A Mobile Peer-to-Peer Recommender System", Master thesis work at Department of Computer and Systems Sciences, Stockholm University, Sweden

Google Scholar

Cited By

View all

Wang ZYu ZZhou XChen CGuo B(2016)Towards Context-Aware Mobile Web BrowsingWireless Personal Communications: An International Journal10.1007/s11277-016-3454-y91:1(187-203)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1007/s11277-016-3454-y
Chen SQian HGu J(2015)A Recommender System for Mobile Commerce Based on Relational LearningMulti-disciplinary Trends in Artificial Intelligence10.1007/978-3-319-26181-2_39(415-428)Online publication date: 29-Nov-2015
https://doi.org/10.1007/978-3-319-26181-2_39
Zhang XYu ZTian JWang ZGuo B(2012)Context-Aware Mobile Web Browsing Based on HTML5Proceedings of the 2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing10.1109/UIC-ATC.2012.107(945-950)Online publication date: 4-Sep-2012
https://dl.acm.org/doi/10.1109/UIC-ATC.2012.107

Index Terms

Context-aware intelligent recommender system
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources

Recommendations

Exploiting Social Tagging in a Web 2.0 Recommender System

Recommender systems help users cope with information overload by using their preferences to recommend items. To date, most recommenders have employed users' ratings, information about the user's profile, or metadata describing the items. To take ...
A General Rating Recommended Weight-Aware Model for Recommendation System
HCC 2016: Revised Selected Papers of the Second International Conference on Human Centered Computing - Volume 9567

In recommendation system, the ratings represent the users' preference and play an important role in recommending items to users. However, the ratings of items may be influenced by many factors, such as time the latest ratings are more able to reflect ...
Context suggestion: empirical evaluations vs user studies
WI '17: Proceedings of the International Conference on Web Intelligence

Recommender System has been successfully applied to assist user's decision making by providing a list of recommended items. Context-aware recommender system additionally incorporates contexts (such as time and location) into the system to improve the ...

Comments

Information & Contributors

Information

Published In

IUI '10: Proceedings of the 15th international conference on Intelligent user interfaces

February 2010

460 pages

ISBN:9781605585154

DOI:10.1145/1719970

General Chairs:
Charles Rich
Worcester Polytechnic Institute, USA
,
Qiang Yang
Hong Kong University of Science & Technology, China
,
Program Chairs:
Marc Cavazza
Teesside University, UK
,
Michelle Zhou
IBM Research, China

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 February 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Demonstration

Conference

IUI '10

Sponsor:

IUI '10: 15th International Conference on Intelligent User Interfaces

February 7 - 10, 2010

Hong Kong, China

Acceptance Rates

Overall Acceptance Rate 746 of 2,811 submissions, 27%

Upcoming Conference

IUI '25

Sponsor:
sigai
sigai

30th International Conference on Intelligent User Interfaces

March 24 - 27, 2025

Cagliari , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
534
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 17 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Wang ZYu ZZhou XChen CGuo B(2016)Towards Context-Aware Mobile Web BrowsingWireless Personal Communications: An International Journal10.1007/s11277-016-3454-y91:1(187-203)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1007/s11277-016-3454-y
Chen SQian HGu J(2015)A Recommender System for Mobile Commerce Based on Relational LearningMulti-disciplinary Trends in Artificial Intelligence10.1007/978-3-319-26181-2_39(415-428)Online publication date: 29-Nov-2015
https://doi.org/10.1007/978-3-319-26181-2_39
Zhang XYu ZTian JWang ZGuo B(2012)Context-Aware Mobile Web Browsing Based on HTML5Proceedings of the 2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing10.1109/UIC-ATC.2012.107(945-950)Online publication date: 4-Sep-2012
https://dl.acm.org/doi/10.1109/UIC-ATC.2012.107

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Exploiting Social Tagging in a Web 2.0 Recommender System

A General Rating Recommended Weight-Aware Model for Recommendation System

Context suggestion: empirical evaluations vs user studies