Recommender Systems. Outline Limitations of Recommender Systems SMARTMUSEUM Case Study.
Recommender-based enhancement of Discovery in...
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RESEARCH STUDIO FOR GEOREFERENCEDMEDIA AND TECHNOLOGY
RESEARCH STUDIOS AUSTRIAStudio iSPACE
Recommender-based enhancement of Discovery in GeoportalsINSPIRE Conference 2012
Bernhard Vockner, Mariana Belgiu, Manfred Mittlböck
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Overview
� Information Discovery
� Objectives
� Recommendations enhancing (Geo)-Web Discovery
� Integration of recommendations based on user
interactions
� Implementation
� Outlook & Discussion
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� Geoportals are main gateways to find, evaluate and start “using”
geographic information.
� Current search in Geoportals:
• Query title, abstract, keywords etc.
• Applying spatial and temporal filters
Information Discovery
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� Geoportals are main gateways to find, evaluate and start “using”
geographic information.
� Current search in Geoportals:
• Query title, abstract, keywords etc.
• Applying spatial and temporal filters
� Consistent efforts towards overcoming semantic heterogeneity
problems
• Thesaurus: GEMET
� User context as well as search results of other users are not
incorporated
Information Discovery
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1990s: catalogues
1998: search engines
~ 2000: RecommenderSystems
Information Discovery
http://www.ukoln.ac.uk/services/papers/follett/arms/lycos.gif
http://blogoscoped.com/files/google-com-history/thumb/1998.jpg
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� Discovery improvement in current Geoportal solutions
by applying well-established discovery methods from
the WWW• Autosuggestion lists
• Tag clouds
• Recommender systems
Approach
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What is a
recommender
system?
Recommendations enhancing (Geo)-Web Discovery
Amazon…….
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Recommendations enhancing (Geo)-Web Discovery
“It is estimated that cross-
selling accounts for
35% of Amazon's revenue”*
*amazon.com 2006
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Recommendations enhancing (Geo)-Web Discovery
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Recommendations enhancing (Geo)-Web Discovery
Personalized
recommendations
Rankings „together“ – Functions
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� Integration of recommendations based on user interactions
• Improvement of discovery in Geoportals by “tracking” user’s interactions with the search results & resources
• Analogy between Geoportal interactions and recommenders:
Recommendation strategies for geo-resources
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Recommendation strategies for geo-resources
rate
view
buy
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Implementation
� Easyrec (OpenSource)
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Implementation
� Easyrec (OpenSource)
� Algorithm: Association Rule Miner (ARM) – Shopping cart analyzer
• Apriori algorithm R (Agrawal & Srikant, 1994):▪ Learning algorithm for association rules
▪ “users loading their cart with item A and item B also put item C in it with a likelihood of 90%”
• SlopeOne (Lemire & Maclachlan, 2005)▪ Item-based collaborative filtering technique
▪ Assumption: behavior of certain groups can be used to conclude on particular interests of individuals
▪ Predict how a user would rate an item based on the ratings of a group of users
� Integration: JavaScript & REST-API• Information provided for recommendations:
▪ Username, Location, ID, title, thumbnail image
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Implementation
� Association Rule Miner (ARM):• is looking for pairs of items < X,Y > that appeared significantly
often together in different baskets
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Implementation
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Outlook & Discussion
� Recommender systems can improve the quality of search results
� Additional “links” between resources based on experiences of other users are established and presented
� Further research to extend recommender-based discovery:
• Incorporation of further elements (e.g. primary search language, domain)
• Integration of contextual similarity of texts▪ Semantic text analysis