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Exploiting Preference Queries for Searching Learning Resources. Fabian Abel, Eelco Herder, Philipp Kärger , Daniel Olmedilla, Wolf Siberski L3S Research Center, Hannover, Germany kaerger@L3S.de. Outline What exactly is a preference? A realistic search scenario How preferences help
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ExploitingPreference Queries forSearching Learning Resources Fabian Abel, Eelco Herder,Philipp Kärger, Daniel Olmedilla, Wolf Siberski L3S Research Center, Hannover, Germany kaerger@L3S.de
Outline • What exactly is a preference? • A realistic search scenario • How preferences help • Prototypical implementation • Conclusions and future work Philipp Kärger - kaerger@L3S.de
First, let’s clarify: What exactly is aPreference ? Philipp Kärger - kaerger@L3S.de
A preference is more than just one preferred value of an attribute • Simple: “I like green and English” • Main assumption: • A preference is an order of values • Better: “I prefer green to red and my last option is brown. I prefer English but German is also fine.” Philipp Kärger - kaerger@L3S.de
“How can this help for technology enhanced learning?” Philipp Kärger - kaerger@L3S.de
Basic example: • “I prefer a cheap course to an expensive one.” • “I prefer to have only a few other participants sharing my course instead of an overcrowded course.” Philipp Kärger - kaerger@L3S.de
20 participants 15 10 5 1 10 20 40 30 price Philipp Kärger - kaerger@L3S.de
Beyond price and number of participants, learners may have lots of preferences: • Language an object is presented in • Where and when does education happen • By which means (e.g., at a computer or in a reading) • Who is teaching/authoring • Type of examination/assessment • Type of interactivity • Text or picture-oriented • … Philipp Kärger - kaerger@L3S.de
2. A realistic search scenario Philipp Kärger - kaerger@L3S.de
v • current search approaches: • conjunctive querying: search for an object bearing all the most preferred attributes • best alternatives act as hard constraints • “return all courses which are on Wednesday AND take 3 monthsAND with no cost AND …” in most of the cases no result Philipp Kärger - kaerger@L3S.de
v • current search approaches: • disjunctive querying: search for an object bearing one of all the given properties • e.g., return courses which take 2 months OR 3 months OR 4 months OR are on Wednesday OR on Monday OR … will return almost all objects as result Philipp Kärger - kaerger@L3S.de
preference solution: • we can make use of the given alternatives for each dimension (e.g., if Wednesday is not possible, I go for Monday) • but which courses are optimal according to the preferences? Philipp Kärger - kaerger@L3S.de
3. How preferences help finding the desired course Philipp Kärger - kaerger@L3S.de
The desired courses are Pareto optimal: A course is optimal if no other course is better (or equal) in all preference dimensions. example: if a course has the same price but more participants than another, it is not optimal. I.e., the first course is pareto-dominated by the second one Philipp Kärger - kaerger@L3S.de
No result bears optimal conditions! Philipp Kärger - kaerger@L3S.de
4. Prototypical Implementation Philipp Kärger - kaerger@L3S.de
test data set: 10,000 lectures held at University Hannover • query language: a novel preference extension of the RDF query language SPARQL • realized as Web Service integrated in the Personal Reader Framework Philipp Kärger - kaerger@L3S.de
User Interface Philipp Kärger - kaerger@L3S.de
5. Conclusions and Future Work Philipp Kärger - kaerger@L3S.de
Conclusions: • Classical search mechanisms consider “preferences” as hard constraints • Problem if no optimal solution exists • Preference-based queries allow for soft constraining the results • pruning the non dominated learning resources dramatically decreases the size of the result set Philipp Kärger - kaerger@L3S.de
Observation: • Users do not need to specify all preferences • Only those they want • Preferences might be automatically extracted • If the student’s schedule is full on Monday then … • If the student’s results are bad for oral exams then … • Default preferences might be turned on • Cheapest price, with certification, lowest distance, highest reputation, etc… Philipp Kärger - kaerger@L3S.de
Future Work • extend preference based search with preference based recommendation • combine this with established collaborative filtering strategies • hybrid solution (e.g., to solve cold start problems) • using preferences in Curriculum planning Philipp Kärger - kaerger@L3S.de
Thanks for your attention. Philipp Kärger L3S Research Center Hannover, Germany kaerger@L3S.de Philipp Kärger - kaerger@L3S.de
