Recommendation models based on collaborative filtering have good prediction accuracy; however, they have very poor performance in cold-start scenarios, when no or few rating data are associated to certain items or users. An approach for dealing with cold-start problem is to build hybrid models that include content-based filtering. This paper focuses on knowledge-based hybrids that exploit seman...

Machine-understandable data constitutes the basis for the Semantic Desktop. We provide in this paper means to author and annotate Semantic Documents on the Desktop. In our approach, the PDF file format is the basis for semantic documents, which store both a document and the related metadata in a single file. To achieve this we provide a framework, SALT that extends the Latex writing environment...

In this paper, we complement the term frequency, which is used in many bag-of-words based information retrieval models, with information about the semantic relatedness of query and document terms. Our experiments show that when employed in the standard probabilistic retrieval model BM25, the additional semantic information significantly outperforms the standard term frequency, and also improves...

The capability of building a model that can be understood and interpreted by humans is one of the main selling points of symbolic machine learning algorithms, such as rule or decision tree learners. However, those algorithms are most often optimized w.r.t. classification accuracy, but not the understandability of the resulting model. In this paper, we focus on a particular aspect of understanda...

many textbooks include semantically related words and sometimes teachers add synonyms, antonyms, etc. to the words in order to present new vocabulary items without questioning the possible effects. this study sought to investigate the effect of teaching vocabulary through synonym, semantically unrelated, and hyponym sets based on higa’s (1963) proposed continuum. a total of 120 iranian intermed...