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  1. P. Massa;P. Avesani,
    Proceedings of ECAI 2006Workshop on Recommender Systems,
    , pp. 29-
    , (ECAI 2006Workshop on Recommender Systems,
  2. P. Avesani;P. Massa;R. Tiella,
    Moleskiing: a Trust-aware Decentralized Recommender System,
    FOAF Workshop at DERI Galway,
    , (1st Workshop on Friend of a Friend, Social Networking and the Semantic Web,
    Galway, Ireland,
  3. P. Massa;P. Avesani,
    Proceedings of the International Conference on Cooperative Information Systems (CoopIS'04),
    , pp. 492-
    , (.,
  4. P. Avesani; A. Agostini,
    A Peer-to-Peer Advertising Game,
    Advertising plays a key role in service oriented recommendation over a peer-to-peer network. The advertising problem can be considered as the problem of finding a common language to denote the peers' capabilities and needs. Up to now the current approaches to the problem of advertising revealed that the proposed solutions either affect the autonomy assumption or do not scale up the size of the network. We explain how an approach based on language games can be effective in dealing with the typical issue of advertising: do not require ex-ante agreement and to be responsive to the evolution of the network as an open system. In the paper, we introduce the notion of advertising game, a specific language game designed to deal with the issue of supporting the emergence of a common denotation language over a network of peers. We provide the related computational model and an experimental evaluation. A positive empirical evidence is achieved by sketching a peer-to-peer recommendation service for bookmark exchanging using real data,
  5. A. Agostini; P. Avesani,
    On the discovery of the semantic context of queries by game-playing,
    To model query answering, a questin arises out of how the meaning of an user's query is functional to get a valuable answer. In this paper, (1) we investigate the question within an existing peer-to-peer architecture for knowledge exchange called KEx, (2) we extend the query answering functionality of KEx by a co-evolutive process based on the user's preference information, (3) we model query answering as a language game,
  6. P. Avesani; C. Girardi; N. Polettini; D. Sona,
    TaxE: a Testbed for Hierarchical Document Classifiers,
    In the last decade the interest in the hierarchical organization of documents is increased. New challenges arise as hierarchical document classification, both unsupervised and supervised. A recognition of the most recent literature on these topics shows that none of the published works refer to the same dataset to enable the experimental phase. Moreover the papers don`t provide enough details to reproduce the same datasets starting from the same information sources. The drawback is twofold: from one hand the waste of time to preprocess suitable datasets, to the other hand the lack of a common testbed to compare alternative solutions. In this paper we propose a dataset extracted from Google and LookSmart web directories to support the experimentation effort in the field of hierarchical document classification. For such a task we aim to provide a kind of reference corpus in analogy with the role that Reuters plays in the scientific community. The paper illustrates the proces! s performed to generate a well defined dataset. This dataset is freely distributed over the web,
  7. S. Veeramachaneni; P. Avesani; E. Olivetti,
    Active feature sampling for low-cost feature evaluation,
    Knowledge discovery is traditionally performed under a tacit closed-world assumption, in that, induction is performed on pre-acquired examples, and the possibility of acquiring additional information is ignored. The active learning paradigm addresses the problem of `intelligently` choosing the unlabelled examples for which the class labels are acquired without modifying the feature space. In contrast we propose a strategy that actively samples the values of new features on class-labeled examples to revise the feature space to perform feature selection among candidate features that have initially not been extracted on any of the examples. The objective is to interleave the acquisition of feature values and the assessment of feature relevance with the ultimate goal of selecting useful features at reduced sampling cost. We present an active feature sampling scheme that enables intelligent data acquisition by accurately predicting the relevance of the feature to the concept!with a reduced number of feature value queries. The optimal selection method, based on maximization of information gain, is approximated by an heuristic algorithm. We demonstrate that active sampling is cost effective in accurately estimating feature relevance. An empirical evaluation on benchmark UCI databases shows that on average our algorithm incurs lower cost compared to random sampling and to a previous active sampling scheme proposed in literature,
  8. E. Olivetti; D. Sona; S. Veeramachaneni,
    Data Analysis for SMAP Project,
    In this document we report the main results obtained analyzing the SMAP data with a statistical approach. We also provide a subjective interpretation of such results,
  9. G. Adami; P. Avesani; D. Sona,
    Clustering Documents into a Web Directory for Bootstrapping a Supervised Classification,
    he management of hierarchically organized data is starting to play a key role in the knowledge management community due to the proliferation of topic hierarchies for text documents. The creation and maintenance of such organized repositories of information requires a great deal of human intervention The machine learning community has partially addressed this problem by developing hierarchical supervised classifiers that help people categorize new resources within given hierarchies. The worst problem of hierarchical supervised classifiers, however, is their high demand in terms of labeled examples. The number of examples required is related to the number of topics in the taxonomy. Bootstrapping a huge hierarchy with a proper set of labeled examples is therefore a critical issue.
    This paper proposes some solutions for the bootstrapping problem, that implicitly or explicitly use taxonomy definition: a baseline approach that classifies documents according to the class labels, and two clustering approaches, whose training is constrained by the a priori knowledge encoded in the taxonomy structure, which consists of both terminological and relational aspects. In particular, we propose the TaxSOM model, that clusters a set of documents in a predefined hierarchy of classes, directly exploiting the knowledge of both their topological organization and their lexical description. Experimental evaluation was performed on a set of taxonomies taken from the Google and LookSmart web directories, obtaining good results
  10. P. Avesani; P. Massa; R. Tiella,
    A Trust-enhanced Recommender System application: Moleskiing,
    Recommender Systems (RS) suggests to users items they will like based on their past ppinions. 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 present a real world application, namely, in which both of these conditions are critic to deliver person- alized recommendations. A blog oriented architecture collects user experiences on ski mountaineering and their opinions on other users. Exploitation of Trust Metrics allows to present only relevant and reliable information according to the user s personal point of view of other authors trustworthiness. Differently from the notion of authority, we claim that trustworthiness is a user centered notion that requires the computation of personalized metrics. We also present an open information exchange architecture that makes use of Semantic Web formats to guarantee interoperability between ski mountaineering communities,