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.

Keywords

machine learning, recommender system, user feedback, context weight

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

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

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1145/1071246.1071289

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