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  1. P. Massa; P. Avesani,
    Trust-aware Similarity Metrics,
    Similarity metrics play a key role in case-based reasoning: an effective retrieval step is a premise for a fruitful reuse of past solutions. In recommendation systems based on collaborative filtering the similarity assessment involves the user profiles. User profiles are very sensitive to the quality of elicited preferences while user similarity doesn t address the issue of quality assessment. We argue that the notion of trust can fruitfully improve the step of similarity assessment. Trust can be conceived as rating persons instead of goods. Some properties of trust, like propagation, allows to overcome known drawbacks of collaborative filtering. We discuss how sparseness of data very often affects the computability of user similarity and we show how trust metrics can overcome this restriction. An empirical evaluation on a real world dataset allow us to argue that trust metrics don t suffer the problem of sparseness and can provide a meaningful improvement in theassessment of user similarity. Moreover we prove that the acquisition of trust values, in contrast with rating values, enables a better trade-off between elicitation effort and similarity accuracy,
  2. S. Veeramachaneni; P. Avesani; E. Olivetti,
    Active Feature Sampling for Cost Constrained Knowledge Discovery,
    When knowledge discovery is viewed as an iterative process wherein the data collection and analysis parts are repeated in sequence, models learnt from the current data can be used to provide support for future data collection. The general approach for driving data collection using information from already acquired data is called active learning. The traditional active learning paradigm addresses the problem of choosing the unlabeled examples for which the class labels are queried without modifying the feature space. In contrast we propose a strategy that actively samples the values of new features on class-labeled examples, with the objective of interleaving the acquisition of feature values and the assessment of feature relevance. We justify our algorithm on information theoretic and statistical grounds. Using an illustrative example we show that our active feature sampling scheme can enable the selection of relevant features with significantly lower data acquisition costs than random sampling,
  3. Paolo Avesani; Sara Ferrari; Angelo Susi,
    Case-Based Ranking for Decision Suppor Systems,
    5th International Conference on Case-Based Reasoning [ICCBR 2003],
    , pp. 35-
    , (5th International Conference on Case-Based Reasoning [ICCBR 2003],
    Trondheim, Norway,
    23/06/2003 - 26/06/2003)
  4. Sriharsha Veeramachaneni; Paolo Avesani,
    Active Sampling for Feature Selection,
    Proceedings of the Third IEEE International Conference on Data Mining,
    , pp. 665-
  5. Giordano Adami; Paolo Avesani; Diego Sona,
    Bootstrapping for Hierarchical Document Classification,
    Proceedings of the Twelfth ACM International COnference on Information & Knowledge Management [CIKM 2003],
    , pp. 295-
  6. Giordano Adami; Paolo Avesani; Diego Sona,
    Clustering Documents in a Web Directory,
    Proceedings of the Fifth ACM Internatinal Workshop on Web Information and Data Management [WIDM 2003],
    , pp. 66-
  7. Paolo Avesani; Alessandro Agostini,
    A Peer-to-Peer Advertising game,
    Service-Oriented Computing. Proceedings of ICSOC 2003,
    , pp. 28-
  8. Alessandro Agostini; Paolo Avesani,
    Advertising gemes for Web Services,
    On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE, Proceedings of the OTM Confederated International Conferences CoopIS, DOA and ODBASE 2003,
    , pp. 93-
  9. Paolo Avesani; Sara Ferrari; Angelo Susi,
    Environmental Risk Assessment as a Case-Based Preference Elicitation Process,
    IJCAI-03 Workshop on Environmental Decision Support Systems [EDDS 2003],
    , pp. 9-
    , (IJCAI-03 Workshop on Environmental Decision Support Systems [EDDS 2003],
    Acapulco, Mexico,
  10. Alessio Micheli; Diego Sona; Alessandro Sperduti,
    Formal Determination of Context in Contextual Recursive Cascade Correlation Networks,
    Artificial Neural Networks and Neural Information Processing – ICANN/ICONIP,
    , (Artificial Neural Networks and Neural Information Processing – ICANN/ICONIP,