daniel lemire, harold boley, sean mcgrath, marcel ball, collaborative filtering and inference rules...

Collaborative Filtering and Inference Rules for Context-Aware Learning Object Recommendation Daniel Lemire ∗ , Harold Boley † , Sean McGrath ‡ , Marcel Ball § September 6, 2005 Abstract Learning objects strive for reusability in e-Learning to re- duce cost and allow perso naliz atio n of content. We ar- gue that learning objects require adapted Information Re- trie val syst ems. In the spirit of the Sema ntic Web, we discuss the semantic description, discovery, and composition of learning objects using Web-based MP3 objects as exampl es. As part of our proj ect , we tag lear nin g objects with both objective (e.g., title, date, and author) and subjective (e.g., quality and relevance) metadata. We study the application of collaborative filtering as proto- typed in the RACOFI (Rule-Applying Collaborative Fil- tering) Composer system, which consists of two libraries and their associated engines: a collaborative filtering system and an inference rule system. We developed RACOFI to generate conte xt-a ware recommend ation list s. Con- text is handled by multidimensional predictions produced from a database-driven scalable collaborative filtering al- gorithm. Rules are then applied to the predictions to cus- tomize the recommendations according to user profiles. The prototype is available at inDiscover.net. 1 Introducti on With the proliferation of the Internet, demand for on-line lea rni ng has grown rapid ly . Oft en use d to del iv er in- expensive just-in-time information, students now expect ∗ UQAM, Montreal, QC, Canada (Work started while at NRC) † NRC, Fredericton, NB, Canada ‡ UNB, Fredericton, NB, Canada § UNB, Fredericton, NB, Canada In International Journal of Interactiv e Technology and Smart Edu- cation, Volume 2, Issue 3, August 2005. simi lar servic es from learnin g institut ions. For teach - ers, these new expectations can be challen ging. The de- sign and production of on-line courses is expensive and time -con sumi ng. When prov idin g digital conte nt, it is no longer adequate to merely manually adjust or adapt the course content for students, students should also be empowered to navigate independently ( Lundgren-Cayrol et al. , 2001). The We b also face s similar chall enges . As the Web becomes ubiqu itou s, our need s become more sophist i- cated . For exa mple , whil e we may have been satis fied in the past with weather reports for a given area, we now want to be able to plan our vacations to other areas and thus, coordinate data coming from weather reports, ho- tels, and air travel companies . While we generally know how to find web sites on a given topic using search engines, we still can’t easily find all air travel companies offering flights from Montréal to Rio next week for under 1000 dolla rs. It follo ws that we can’t expec t our com- puters to suggest travel packages automatically from data gath ered over the Web . We obse rve that the Web is not a database (Mendelzon, 1998) in that there are no built in common schemas or sophisticated data retrieval mech- anism. Yet the Web is the most succes sfu l dat a man - agement tool ever developed. We distinguish two differ- ent future challenges for the Web: Information Retrie val and Compos itio n. One approa ch to the solut ion to these problems can be found in the Semantic Web (Berners-Lee, 1998). Essentially, the Semantic Web adds to the current Web enough metadata so that the Web can be considered machine-parsea ble (Koivunen & Miller, 2001). In theory, it should render the Information Retrieval problem easier, and one approach to Composition can then be achieved through Artificial Intelligence (AI) planning techniques. 1

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Page 1: Daniel Lemire, Harold Boley, Sean McGrath, Marcel Ball, Collaborative Filtering and Inference Rules for Context-Aware Learning Object Recommendation, International Journal of Interactive

8/14/2019 Daniel Lemire, Harold Boley, Sean McGrath, Marcel Ball, Collaborative Filtering and Inference Rules for Context-A…

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http://www.knowledgeagora.com/

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http://elera.net/

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http://racofi.elg.ca/

http://www.ruleml.org/

http://www.jdrew.org/oojdrew/

http://savannah.nongnu.org/projects/cofi/

http://sifter.elg.ca/

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http://www.amazon.com/

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http://www.indiscover.net/

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