Semi-supervised learning of user-preferred travel schedules

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a graph-based semi-supervised approach for learning user-preferred travel schedules. Assuming a setting in which a user provides a small number of labeled travel schedules, we classify schedules into desirable and non-desirable. This task is non-trivial since only a small number of labeled points is available. It is further complicated by the fact that each schedule is comprised of multiple components or aspects which are different in nature. For instance in our case arrival times are modeled by probability distributions to account for uncertainty, while other aspects such as waiting times are given by a feature vector. Each aspect can thought of as a different type of observation for the same schedule While existing label propagation approaches can exploit vast amounts of unlabeled data, they cannot handle multi-aspect data. We propose Multi-Aspect Label Propagation (MALP), a novel approach which extends label propagation to handle multiple types of observations.

Original languageEnglish (US)
Title of host publication8th International Joint Conference on Autonomous Agents and Multiagent Systems 2009, AAMAS 2009
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1196-1197
Number of pages2
ISBN (Print)9781615673346
StatePublished - 2009
Event8th International Joint Conference on Autonomous Agents and Multiagent Systems 2009, AAMAS 2009 - Budapest, Hungary
Duration: May 10 2009May 15 2009

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume2
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Other

Other8th International Joint Conference on Autonomous Agents and Multiagent Systems 2009, AAMAS 2009
CountryHungary
CityBudapest
Period5/10/095/15/09

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