We propose a simple model of an idealized online cultural market in which N items, endowed with a hidden quality metric, are recommended to users by a ranking algorithm possibly biased by the current items’ popularity. Our goal is to better understand the underlying mechanisms of the well-known fact that popularity bias can prevent higher-quality items from becoming more popular than lower-quality items, producing an undesirable misalignment between quality and popularity rankings. We do so under the assumption that users, having limited time/attention, are able to discriminate the best-quality only within a random subset of the items. We discover the existence of a harmful regime in which improper use of popularity can seriously compromise the emergence of quality, and a benign regime in which wise use of popularity, coupled with a small discrimination effort on behalf of users, guarantees the perfect alignment of quality and popularity ranking. Our findings clarify the effects of algorithmic popularity bias on quality outcomes, and may inform the design of more principled mechanisms for techno-social cultural markets.

Signed networks contain edge annotations to indicate whether each interaction is friendly (positive edge) or antagonistic (negative edge). The model is simple but powerful and it can capture novel and interesting structural properties of real-world phenomena. The analysis of signed networks has many applications from modeling discussions in social media, to mining user reviews, and to recommending products in e-commerce sites. In this paper we consider the problem of discovering polarized communities in signed networks. In particular, we search for two communities (subsets of the network vertices) where within communities there are mostly positive edges while across communities there are mostly negative edges. We formulate this novel problem as a "discrete eigenvector'' problem, which we show to be NP-hard. We then develop two intuitive spectral algorithms: one deterministic, and one randomized with quality guarantee $\sqrtn $ (where n is the number of vertices in the graph), tight up to constant factors. We validate our algorithms against non-trivial baselines on real-world signed networks. Our experiments confirm that our algorithms produce higher quality solutions, are much faster and can scale to much larger networks than the baselines, and are able to detect ground-truth polarized communities.

Thanks to the advancements in ubiquitous and wearable technologies, Personal Informatics (PI) systems can now reach a larger audience of users. However, it is not still clear whether this kind of tool can fit the needs of their daily lives. Our research aims at identifying specific barriers that may prevent the widespread adoption of PI and finding solutions to overcome them. We requested users without competence in self-tracking to use different PI instruments during their daily practices, identifying five user requirements by which to design novel PI tools. On such requirements, we developed a new system that can stimulate the use of these technologies, by enhancing the perceived benefits of collecting personal data. Then, we explored how naïve and experienced users differently explore their personal data in our system through a user trial. Results showed that the system was successful at helping individuals manage and interpret their own data, validated the usefulness of the requirements found and inspired three further design opportunities that could orient the design of future PI systems.

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 article, 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 Networks” (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.

The lack of a trusted authority, responsible for peers' identity verification or for authentication purposes, makes actual P2P systems extremely vulnerable to a large spectrum of attacks. The main purpose of this paper is to present Likir (layered identity-based Kademlia-like infrastructure), a framework that includes an identity-based scheme and a secure communication protocol, built on top of Kademlia, that may provide an effective defense against well known attacks. This will be accomplished with the adoption of a certification service, with the use of an authentication protocol between nodes and with the introduction of credentials to make non-repudiable the ownership of the contents and messages inserted in the DHT. For sake of interoperability with other social networking services, Likir enables identity management under the Identity 2.0 framework. Under this perspective, the IBS (identity-based signature) scheme is taken into consideration and analyzed as well.

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 a complete knowledge scenario; secondly, we propose epidemic collaborative strategies that can reduce rapidly and realistically the cold start problem.

The "small world"-paradigm offers a new interesting view-point for the analysis of contemporary legal networks and artificial intelligence. This topological approach sheds further light on such different fields as case-based legal reasoning, knowledge discovery in legal databases, or legal ontologies, as far as clustering coefficients, diameter and hubs of the network are involved. Moreover, empirical evidence shows that even P2P systems as Gnutella present small world-features. So it becomes possible to deepen our understanding of how spontaneous communities organize themselves in the network. While opening new horizons in the field of recommender systems, it also widens our perspective in dealing with such important issues as privacy and digital copyright.

We present WALTy (Web application load-based testing tool), a set of tools that allows the performance analysis of Web applications by means of a scalable what-if analysis on the test bed. The proposed approach is based on a workload characterization generated from information extracted from log files. The workload is generated by using of customer behavior model graphs (CBMG), that are derived by extracting information from the Web application log files. In this manner the synthetic workload used to evaluate the Web application under test is representative of the real traffic that the Web application has to serve. One of the most common critics to this approach is that synthetic workload produced by Web stressing tools is far from being realistic. The use of the CBMGs might be useful to overcome this critic.

The important problem of user password selection is addressed and a new proactive password-checking technique is presented. In a training phase, a decision tree is generated based on a given dictionary of weak passwords. Then, the decision tree is used to determine whether a user password should be accepted. Experimental results described here show that the method leads to a very high dictionary compression (up to 1000 to 1) with low error rates (of the order of 1%). A prototype implementation, called ProCheck, is made available online. We survey previous approaches to proactive password checking, and provide an in-depth comparison.

The important problem of user password selection is addressed and a new proactive password checking technique is presented.In a training phase, a decision tree is generated based on a given dictionary of weak passwords.Then, the decision tree is used to determine whether a user password should be accepted.Experimental results described here show that the method leads to very high dictionary compression (from 100 to 3 in the average) with low error rates (of the order of 1%).We survey previous approaches to proactive password checking, and provide an in-depth comparison.