Publication Type
Conference Proceeding Article
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.
Discipline
Databases and Information Systems
Research Areas
Information Systems and Management
Publication
Proceedings of the 6th International Conference on Mobile Data Management (MDM'05), Ayia Napa Cyprus, May 9 - 13
First Page
265
Last Page
272
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). Proceedings of the 6th International Conference on Mobile Data Management (MDM'05), Ayia Napa Cyprus, May 9 - 13. 265-272.
Available at: https://ink.library.smu.edu.sg/sis_research/6567
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/1071246.1071289