In the following there is a list of publications related to the DeHinter project:

• G.Ruffo, R.Schifanella   "A Peer-to-Peer Recommender System Based on Spontaneous Affinities ", ACM Transactions on Internet Technology (TOIT), ACM Press, New York, NY, USA. (to appear). abstract

  Network analysis has proved to be very useful in many social and natural sciences, and in particular Small World topologies have been exploited in many application fields. In this paper, we focus on P2P file sharing applications, where spontaneous communities of users are studied and analyzed. We define a family of structures that we call "Affinity Network" (or even Graphs) that show self organized interest-based clusters. Empirical evidence proves that affinity networks are small worlds and shows scale-free features. The relevance of this finding is augmented with the introduction of a proactive recommendation scheme, namely DeHinter, that exploits this natural feature. The intuition behind this scheme is that a user would trust her network of "elective affinities" more than anonymous and generic suggestions made by impersonal entities. The accuracy of the recommendation is evaluated by way of a 10-fold cross validation, and a prototype has been implemented for further feedbacks from the users.

• G. Ruffo, R. Schifanella, E. Ghiringhello   "A Decentralized Recommendation System based on Self-Organizing Partnerships", IFIP-Networking 2006, May 2006, Coimbra (Portugal) LNCS 3976, pp. 618-629, Springer-Verlag, 2006.   abstract | pdf | link

  Small World patterns have been found in many social and natural networks, and even in Peer-to-Peer topologies. In this paper, we analyze File Sharing applications that aggregate virtual communities of users exchanging data. In these domains, it is possible to define overlaying structures that we call “Preference Networks” that show self organized interest-based clusters. The relevance of this finding is augmented with the introduction of a proactive recommendation scheme that exploits this natural feature. The intuition behind this scheme is that a user would trust her network of “elective affinities” more than anonymous and generic suggestions made by impersonal entities.

• G.Ruffo, R.Schifanella   "Evaluating Peer-to-Peer Recommender Systems that Exploit Spontaneous Affinities ", In 22nd Annual ACM Symposium on Applied Computing (SAC) March 11 - 15, 2007, Seoul, Korea. ACM Press.   abstract | pdf | link

  The validation of a recommender system is always a quite hazardous task, because of the difficulty of modeling the tastes of a given user. Novel (decentralized) recommender systems are proposed and evaluated by way of well known logs of user profiles and buddy tables, that contain lists of items with feedback ratings assigned by a given set of users. These information are cross linked, and the precision of the recommendation is compared with other well known (centralized) systems. This evaluation approach cannot be applied in the actual peer-to-peer domain: it is difficult, if not impossible, to build and maintain user profiles, and users are not required to give feedbacks to a data collector entity. Moreover, objects are poorly or not structured, and meta-information, when present, cannot be trusted because of fake files and incomplete item descriptions. In this paper, we present an evaluation process based on a 10-fold cross validation task, that we applied to estimate accuracy of the suggestions of a P2P recommender system recently proposed. The complexity of the evaluation of this peculiar recommender is increased because of ``spontaneous affinities'' between users that are used instead of classical knowledge representation based strategies.

Other publications in the recommeder systems area can be found here:

• A. Panisson, G. Ruffo and R. Schifanella   "X-Hinter: a Framework for Implementing Social Oriented Recommender Systems", in Proc. of the 21th ACM Conference on Hypertext and Hypermedia (Hypertext 2008), June 19-21, 2008, Pittsburgh, Pennsylvania, USA. ACM Press.  abstract | pdf | link

  Accordingly to the nature of data-driven applications that produce information overload, users need a support to make choices, even without sufficient personal experience of the alternatives. In this context, social networking techniques could be useful applied for finding affinities between users and filter information in a personalized way. After proposing a generalized model for social recommender systems, called X-Hinter, we describe a Java API that provides a set of libraries and tools to build social filtering systems in a wide range of domains. A prototype implementation, named DeHinter, shows the feasibility of the proposed approach in a P2P file sharing application.

• R. Schifanella, A. Panisson, C. Gena and G. Ruffo   "MobHinter: Epidemic Collaborative Filtering and Self-Organization in Mobile Ad-Hoc Networks", in Proc. of the 2nd ACM International Conference on Recommender Systems (RecSys 2008), October 23-25, 2008, Lausanne, Switzerland. ACM Press (to appear).  abstract

  We focus on collaborative filtering dealing with self-organizing communities, host mobility, wireless access, and Ad-Hoc communications. In such a domain, knowledge representation and users profiling can be hard; remote servers can be often unreachable due to client mobility; and feedback ratings collected during random connections to other users' ad-hoc devices can be useless, because of natural differences between human beings. Our approach is based on so called Affinity Networks, and on a novel system, called MobHinter, that epidemically spreads recommendations through spontaneous similarities between users. Main results of our study are two fold: firstly we show how to reach comparable recommendation accuracies in the mobile domain as well as in the centralized scenario; secondly, we propose epidemic collaborative strategies that can reduce rapidly and realistically the cold start problem.