OWL2.0 Primer Part02

RDFa Basics

OWL 2.0 Primer Part01

Hdd industry

Owl 2.0 Overview

SAC2016-Measuring Semantic Distance for Linked Open Data-enabled Recommender Systems

UMAP2016EA - Analyzing MOOC Entries of Professionals on LinkedIn for User Modeling and Personalized MOOC Recommendations

Analyzing User Modeling on Twitter for Personalized News Recommendations

JIST2015-Computing the Semantic Similarity of Resources in DBpedia for Recommendation Purposes

EKAW2016 - Interest Representation, Enrichment, Dynamics, and Propagation: A Study of the Synergetic Effect of Different User Modeling Dimensions for Personalized Recommendations on

UMAP2016 - Analyzing Aggregated Semantics-enabled User Modeling on Google+ and Twitter for Personalized Link Recommendations

SEMANTiCS2016 - Exploring Dynamics and Semantics of User Interests for User Modeling on Twitter for Link Recommendations

Retweet Prediction with Attention-based Deep Neural Network

WISE2017 - Factorization Machines Leveraging Lightweight Linked Open Data-enabled Features for Top-N Recommendations

Hypertext2017-Leveraging Followee List Memberships for Inferring User Interests for Passive Users on Twitter

ECIR2017-Inferring User Interests for Passive Users on Twitter by Leveraging Followee Biographies

JIST2015-data challenge