Learning and engineering similarity functions for business recommenders

Type
04B - Conference paper
Editors
Gerber, Aurona
Lenat, Doug
Harmelen, Frank van
Clark, Peter
Editor (Corporation)
Supervisor
Parent work
Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)
Special issue
DOI of the original publication
Series
Series number
Volume
Issue / Number
Pages / Duration
Patent number
Publisher / Publishing institution
Place of publication / Event location
Palo Alto
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
We study the optimisation of similarity measures in tasks where the computation of similarities is not directly visible to end users, namely clustering and case-based recommenders. In both, similarity plays a crucial role, but there are also other algorithmic components that contribute to the end result. Our suggested approach introduces a new form of interaction into these scenarios that make the use of similarities transparent to end users and thus allows to gather direct feedback about similarity from them. This happens without distracting them from their goal – rather allowing them to obtain better and more trustworthy results by excluding dissimilar items. We then propose to use the feedback in a way that incorporates machine learning for updating weights and decisions of knowledge engineers about possible additional features, based on insights derived from a summary of user feedback. The reviewed literature and our own previous empirical investigations suggest that this is the most feasible way – involving both machine and human, each in a task that they are particularly good at.
Keywords
Subject (DDC)
330 - Wirtschaft
Project
Event
AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 20119)
Exhibition start date
Exhibition end date
Conference start date
25.03.2019
Conference end date
27.03.2019
Date of the last check
ISBN
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Diamond
License
'https://creativecommons.org/licenses/by/4.0/'
Citation
WITSCHEL, Hans Friedrich und Andreas MARTIN, 2019. Learning and engineering similarity functions for business recommenders. In: Andreas MARTIN, Knut HINKELMANN, Aurona GERBER, Doug LENAT, Frank van HARMELEN und Peter CLARK (Hrsg.), Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019). Palo Alto. 2019. Verfügbar unter: https://doi.org/10.26041/fhnw-6789