Marco de Gemmis

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As proved by the continuous growth of the number of web sites which embody recommender systems as a way of personalizing the experience of users with their content, recommender systems represent one of the most popular applications of principles and techniques coming from Information Filtering (IF). As IF techniques usually perform a progressive removal of(More)
This paper provides an overview of the work done in the Linked Open Data-enabled Recommender Systems challenge, in which we proposed an ensemble of algorithms based on popularity, Vector Space Model, Random Forests, Logistic Regression, and PageRank, running on a diverse set of semantic features. We ranked 1st in the top-N recommendation task, and 3rd in(More)
Natural Language Processing (NLP) has a significant impact on many relevant Web-based and Semantic Web applications, such as information filtering and retrieval. Tools supporting the development of NLP applications are playing a key role in textbased information access on the Web. In this paper, we present META (MultilanguagE Text Analyzer), a tool for text(More)
Canonical Information Retrieval systems perform a ranked keyword search strategy: Given a user’s one-off information need (query), a list of documents, ordered by relevance, is returned. The main limitation of that “one fits all” approach is that long-term user interests are neglected in the search process, implicitly assuming that they are completely(More)
The exponential growth of the Web is the most influential factor that contributes to the increasing importance of cross-lingual text retrieval and filtering systems. Indeed, relevant information exists in different languages, thus users need to find documents in languages different from the one the query is formulated in. In this context, an emerging(More)
Nowadays Web sites tend to be more and more social: users can upload any kind of information on collaborative platforms and can express their opinions about the content they enjoyed through textual feedbacks or reviews. These platforms allow users to annotate resources they like through freely chosen keywords (called tags). The main advantage of these tools(More)
The exponential growth of the Web is the most influential factor that contributes to the increasing importance of cross-lingual text retrieval and filtering systems. Indeed, relevant information exists in different languages, thus users need to find documents in languages different from the one the query is formulated in. In this context, an emerging(More)
Today recommenders are commonly used with various purposes, especially dealing with ecommerce and information filtering tools. Content-based recommenders rely on the concept of similarity between the bought/searched/visited item and all the items stored in a repository. It is a common belief that the user is interested in what is similar to what she has(More)
This paper presents a WSD strategy which combines a knowledge-based method that exploits sense definitions in a dictionary and relations among senses in a semantic network, with supervised learning methods on annotated corpora. The idea behind the approach is that the knowledge-based method can cope with the possible lack of training data, while supervised(More)