Incremental Learning in a Probalistic Information Retrieval System
Abstract
Current technologies have increased both the quantity of information available and the modes of access to it. General tools to provide access should be adaptable to individual contexts and needs. Our research involves the use of learning and adaptive techniques to improve the quality of an IR System. We outline a fully implemented experimental IRS (Okapi), which uses search term weighting and item ranking, based on a probabilistic model. One of its current deficiencies is that users do not benefit from continual use, since the system does not adapt to particular users or their search topics. Here we describe an incremental learning algorithm which builds contextual linkages from user sessions so as to optimise the order of reference display and enhance the relevance of reference listings.
Cite
Text
Goker and McCluskey. "Incremental Learning in a Probalistic Information Retrieval System." International Conference on Machine Learning, 1991. doi:10.1016/B978-1-55860-200-7.50054-4Markdown
[Goker and McCluskey. "Incremental Learning in a Probalistic Information Retrieval System." International Conference on Machine Learning, 1991.](https://mlanthology.org/icml/1991/goker1991icml-incremental/) doi:10.1016/B978-1-55860-200-7.50054-4BibTeX
@inproceedings{goker1991icml-incremental,
title = {{Incremental Learning in a Probalistic Information Retrieval System}},
author = {Goker, A. and McCluskey, Thomas Leo},
booktitle = {International Conference on Machine Learning},
year = {1991},
pages = {255-259},
doi = {10.1016/B978-1-55860-200-7.50054-4},
url = {https://mlanthology.org/icml/1991/goker1991icml-incremental/}
}