The huge amount of interlinked information referring to different domains, provided by the Linked Open Data (LOD) initiative, could be e↵ectively exploited by recommender systems to deal with the cold-start and sparsity problems. In this paper we investigate the contribution of several features extracted from the Linked Open Data cloud to the accuracy of di↵erent recommendation algorithms. We f...

Online debate forums are important social media for people to voice their opinions and debate with each other. Mining user stances or viewpoints from these forums has been a popular research topic. However, most current work does not address an important problem: for a specific issue, there may not be many users participating and expressing their opinions. Despite the sparsity of user stances, ...

We present results on combining supervised and unsupervised methods to ensemble multiple systems for two popular Knowledge Base Population (KBP) tasks, Cold Start Slot Filling (CSSF) and Tri-lingual Entity Discovery and Linking (TEDL). We demonstrate that our combined system along with auxiliary features outperforms the best performing system for both tasks in the 2015 competition, several ense...

We present KELVIN, an automated system for processing a large text corpus and distilling a knowledge base about persons, organizations, and locations. We have tested the KELVIN system on several corpora, including: (a) the TAC KBP 2012 Cold Start corpus which consists of public Web pages from the University of Pennsylvania, and (b) a subset of 26k news articles taken from English Gigaword 5th e...

Recommender systems help users to find information that best fits their preferences and needs in an overloaded search space. Most recommender systems research has been focused on the accuracy improvement of recommendation algorithms. Despite this, recently new trends in recommender systems have become important research topics such as, cold start, group recommendations, context-aware recommenda...

The paper describes an ontology-based approach for integrating interactive user modeling and learning content management to deal with typical adaptation problems, such as cold start and dynamics of the user’s knowledge, in the context of the Semantic Web. An integrated OntoAIMS system is presented and its viability discussed based on user studies. The work demonstrates some novel aspects, such ...

Personalised social matching systems can be seen as recommender systems that recommend people to others in the social networks. However, with the rapid growth of users in social networks and the information that a social matching system requires about the users, recommender system techniques have become insufficiently adept at matching users in social networks. This paper presents a hybrid soci...

Bayesian treatment of matrix factorization has been successfully applied to the problem of collaborative prediction, where unknown ratings are determined by the predictive distribution, inferring posterior distributions over user and item factor matrices that are used to approximate the user-item matrix as their product. In practice, however, Bayesian matrix factorization suffers from cold-star...

Recommender systems apply knowledge discovery techniques to help in finding associated information. In this paper, we investigate the use of association rule mining as an underlying technology for a recommender system aimed at improving the annotation process of multimedia news documents. The accuracy of these systems is very sensitive to the number of already annotated news items (the ”cold-st...