Most of current recommendation systems use numerical ratings to suggest a content (e.g. movie, restaurant) to a user. Instead, we apply probabilistic topic models to text reviews. We profile contents in a latent space where we compute distances that can be used for cold start recommendation.

Collaborative filtering systems are probably the most known recommendation techniques in the recommender systems field. They have been deployed in many commercial and academic applications. However, these systems still have some limitations such as cold start and sparsty problems. Recently, exploiting semantic web technologies such as social recommendations and semantic resources have been inve...

Various forms of Peer-Learning Environments are increasingly being used in post-secondary education, often to help build repositories of student generated learning objects. However, large classes can result in an extensive repository, which can make it more challenging for students to search for suitable objects that both reflect their interests and address their knowledge gaps. Recommender Sys...

In this article, we analyze tag-based user profiles, which result from social tagging activities in Social Web systems and particularly in Flickr, Twitter and Delicious. We investigate the characteristics of tag-based user profiles within these systems, examine to what extent tag-based profiles of individual users overlap between the systems and identify significant benefits of cross-system use...

Our team from the JHU HLTCOE participated in the Entity Linking and Cold Start Knowledge Base tasks in this year’s Text Analysis Conference Knowledge Base Population evaluation. We have previously participated in TAC-KBP entity linking evaluations in 2009, 2010, and 2011. This year we developed two new systems: CALE (Context Aware Linker of Entities) and KELVIN (Knowledge Extraction, Linking, V...

Link prediction in multi-relational social networks has attracted much attention. For instance, we may care the chance of two users being friends based on their contacts of other patterns, e.g., SMS and phone calls. In previous work, matrix factorization models are typically applied in single-relational networks; however, two challenges arise to extend it into multi-relational networks. First, ...