Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2005
Abstract
Traditional approaches to recommender systems have not taken into account situational information when making recommendations, and this seriously limits the relevance of the results. This paper advocates context-awareness as a promising approach to enhance the performance of recommenders, and introduces a mechanism to realize this approach. We present a framework that separates the contextual concerns from the actual recommendation module, so that contexts can be readily shared across applications. More importantly, we devise a learning algorithm to dynamically identify the optimal set of contexts for a specific recommendation task and user. An extensive series of experiments has validated that our system is indeed able to learn both quickly and accurately.
Keywords
machine learning, recommender system, user feedback, context weight
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Publication
MDM '05: Proceedings: Sixth International Conference on Mobile Data Management: May, 9-13, 2005, Ayia Napa, Cyprus
First Page
265
Last Page
272
ISBN
9781595930415
Identifier
10.1145/1071246.1071289
Publisher
ACM
City or Country
New York
Citation
YAP, Ghim-Eng; TAN, Ah-Hwee; and PANG, Hwee Hwa.
Dynamically Optimized Context in Recommender Systems. (2005). MDM '05: Proceedings: Sixth International Conference on Mobile Data Management: May, 9-13, 2005, Ayia Napa, Cyprus. 265-272.
Available at: https://ink.library.smu.edu.sg/sis_research/1137
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1145/1071246.1071289
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons