Paolo Avesani

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Recommender Systems based on Collaborative Filtering suggest to users items they might like. However due to data sparsity of the input ratings matrix, the step of finding similar users often fails. We propose to replace this step with the use of a trust metric, an algorithm able to propagate trust over the trust network and to estimate a trust weight that(More)
Recommender Systems allow people to find the resources they need by making use of the experiences and opinions of their nearest neighbours. Costly annotations by experts are replaced by a distributed process where the users take the initiative. While the collaborative approach enables the collection of a vast amount of data, a new issue arises: the quality(More)
In today’s connected world it is possible and very common to interact with unknown people, whose reliability is unknown. Trust Metrics are a recently proposed technique for answering questions such as “Should I trust this user?”. However, most of the current research assumes that every user has a global quality score and that the goal of the technique is(More)
In today’s connected world it is possible and indeed quite common to interact with unknown people, whose reliability is unknown. Trust Metrics are a technique for answering questions such as “Should I trust this person?”. However, most of the current research assumes that every user has a global quality score everyone agree on and the goal of the technique(More)
Recommender Systems (RS) suggests to users items they will like based on their past opinions. Collaborative Filtering (CF) is the most used technique to assess user similarity between users but very often the sparseness of user profiles prevents the computation. Moreover CF doesn't take into account the reliability of the other users. In this paper we(More)
Recommender Systems (RS) suggest to users items they might like such as movies or songs. However they are not able to generate recommendations for users who just registered, in fact bootstrapping Recommender Systems for new users is still an open challenge. While traditional RSs exploit only ratings provided by users about items, Trust-aware Recommender(More)
Recommender Systems (RS) [25] have the goal of suggesting to every user the items that might be of interest for her. In particular, RSs based on Collaborative Filtering (CF) [2] rely on the opinions expressed by the other users. In fact, CF tries to automatically find users similar to the active one and recommends to this active user the items liked by(More)
Matching hierarchical structures, like taxonomies or web directories, is the premise for enabling interoperability among heterogenous data organizations. While the number of new matching solutions is increasing the evaluation issue is still open. This work addresses the problem of comparison for pairwise matching solutions. A methodology is proposed to(More)
Several techniques are currently used to evaluate recommender systems. These techniques involve off-line analysis using evaluation methods from machine learning and information retrieval. We argue that while off-line analysis is useful, user satisfaction with a recommendation strategy can only be measured in an on-line context. We propose a new evaluation(More)
The proliferation of text documents on the web as well as within institutions necessitates their convenient organization to enable efficient retrieval of information. Although text corpora are frequently organized into concept hierarchies or taxonomies, the classification of the documents into the hierarchy is expensive in terms human effort. We present a(More)