Knowledge Planning and Learned Personalization for Web-Based Case Adaptation

Leake, David; Powell, Jay

doi:10.1007/978-3-540-85502-6_19

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…However, to the exception of [7] , those works generally avoid synthesizing or recommending new paths and instead focus solely on the analysis itself. For the latter, a number of other works construct and recommend tourist itineraries at various granularities [3,4]. They rely, however, on structured and cleansed data on landmarks, and do not deal with the challenge of analyzing and extracting from noisy data.…”

Section: Related Workmentioning

confidence: 99%

Constructing travel itineraries from tagged geo-temporal breadcrumbs

Choudhury

Feldman

Amer-Yahia

et al. 2010

Proceedings of the 19th International Conference on World Wide Web

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Vacation planning is a frequent laborious task which requires skilled interaction with a multitude of resources. This paper develops an end-to-end approach for constructing intra-city travel itineraries automatically by tapping a latent source reflecting geo-temporal breadcrumbs left by millions of tourists. In particular, the popular rich media sharing site, Flickr, allows photos to be stamped by the date and time of when they were taken, and be mapped to Points Of Interest (POIs) by latitude-longitude information as well as semantic metadata (e.g., tags) that describe them.Our extensive user study on a "crowd-sourcing" marketplace (Amazon Mechanical Turk), indicates that high quality itineraries can be automatically constructed from Flickr data, when compared against popular professionally generated bus tours.

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Section: Related Workmentioning

confidence: 99%

Constructing travel itineraries from tagged geo-temporal breadcrumbs

Choudhury

Feldman

Amer-Yahia

et al. 2010

Proceedings of the 19th International Conference on World Wide Web

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“…According to Leake and Powell (2008), how to endow case-based reasoning systems with effective case adaptation capabilities is a classic problem. A significant impediment to developing automated adaptation procedures is the difficulty of acquiring the required knowledge.…”

mentioning

confidence: 99%

On Application of Case-Based Reasoning to Personalise Learning

Mamčenko¹,

Kurilovas²,

Krikun³

2019

Informatics in Education

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The paper aims to present application of Educational Data Mining and particularly Case-Based Reasoning (CBR) for students profiling and further to design a personalised intelligent learning system. The main aim here is to develop a recommender system which should help the learners to create learning units (scenarios) that are the most suitable for them. First of all, systematic literature review on application of CBR and its possible implementation to personalise learning was performed in the paper. After that, methodology on CBR application to personalise learning is presented where learning styles play a dominate role as key factor in proposed personalised intelligent learning system model based on students profiling and personalised learning process model. The algorithm (the sequence of steps) to implement this model is also presented in the paper.

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