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OWL2.0 Primer Part02
OWL2.0 Primer Part02
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RDFa Basics
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SAC2016-Measuring Semantic Distance for Linked Open Data-enabled Recommender Systems
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UMAP2016EA - Analyzing MOOC Entries of Professionals on LinkedIn for User Modeling and Personalized MOOC Recommendations
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SEMANTiCS2016 - Exploring Dynamics and Semantics of User Interests for User Modeling on Twitter for Link Recommendations
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Analyzing User Modeling on Twitter for Personalized News Recommendations
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JIST2015-Computing the Semantic Similarity of Resources in DBpedia for Recommendation Purposes
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EKAW2016 - Interest Representation, Enrichment, Dynamics, and Propagation: A Study of the Synergetic Effect of Different User Modeling Dimensions for Personalized Recommendations on
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UMAP2016 - Analyzing Aggregated Semantics-enabled User Modeling on Google+ and Twitter for Personalized Link Recommendations
UMAP2016 - Analyzing Aggregated Semantics-enabled User Modeling on Google+ and Twitter for Personalized Link Recommendations
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Retweet Prediction with Attention-based Deep Neural Network
Retweet Prediction with Attention-based Deep Neural Network
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WISE2017 - Factorization Machines Leveraging Lightweight Linked Open Data-enabled Features for Top-N Recommendations
WISE2017 - Factorization Machines Leveraging Lightweight Linked Open Data-enabled Features for Top-N Recommendations
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Hypertext2017-Leveraging Followee List Memberships for Inferring User Interests for Passive Users on Twitter
Hypertext2017-Leveraging Followee List Memberships for Inferring User Interests for Passive Users on Twitter
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ECIR2017-Inferring User Interests for Passive Users on Twitter by Leveraging Followee Biographies
ECIR2017-Inferring User Interests for Passive Users on Twitter by Leveraging Followee Biographies
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JIST2015-data challenge
JIST2015-data challenge
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