Paolo Massa

Trust-aware recommender systems

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 can be used in place of the similarity weight. An empirical evaluation on Epinions.com dataset shows that Recommender Systems that make use of trust information are the most effective in term of accuracy while preserving a good coverage. This is especially evident on users who provided few ratings.

Trust-Aware Collaborative Filtering for Recommender Systems

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 assessment. The elicitation of trust values among users, termed “web of trust”, allows a twofold enhancement of Recommender Systems. Firstly, the filtering process can be informed by the reputation of users which can be computed by propagating trust. Secondly, the trust metrics can help to solve a problem associated with the usual method of similarity assessment, its reduced computability. An empirical evaluation on Epinions.com dataset shows that trust propagation can increase the coverage of Recommender Systems while preserving the quality of predictions. The greatest improvements are achieved for users who provided few ratings.

Using Trust in Recommender Systems: An Experimental Analysis

Recommender systems (RS) have been used for suggesting items (movies, books, songs, etc.) that users might like. RSs compute a user similarity between users and use it as a weight for the users’ ratings. However they have many weaknesses, such as sparseness, cold start and vulnerability to attacks. We assert that these weaknesses can be alleviated using a Trust-aware system that takes into account the “web of trust” provided by every user.

Trust Metrics on Controversial Users: Balancing Between Tyranny of the Majority

In today’s connected world, it is possible and indeed quite common to interact with un-known 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 agrees on and the goal of the technique is just to predict this correct value. We show on data from a real and large user community, Epinions.com, that such an assumption is not realistic because there is a significant portion of what we call controversial users, users who are trusted by many and distrusted by many: a global agreement about the trustworthiness value of these users does not exist. We argue, using computational experiments, that the existence of controversial users (a normal phenomenon in complex societies) demands local trust metrics, techniques able to predict the trustworthiness of a user in a personalized way, depending on the very personal views of the judging user as opposed to most commonly used global trust metrics, which assume a unique value of reputation for every single user. The implications of such an analysis deal with the very foundations of what we call society and culture and we conclude discussing this point by comparing the two extremes of culture that can be induced by the two different kinds of trust metrics: tyranny of the majority and echo chambers.

Page-reRank: Using Trusted Links to Re-Rank Authority

Search engines like Google.com use the link structure of the Web to determine whether Web pages are authoritative sources of information. However, the linking mechanism provided by HTML does not allow the Web author to express different types of links, such as positive or negative endorsements of page content. As a consequence, search engine algorithms cannot discriminate between sites that are highly linked and sites that are highly trusted. We demonstrate our claim by running PageRank on a real world data set containing positive and negative links. We conclude that simple semantic extensions to the link mechanism would provide a richer semantic network from which to mine more precise Web intelligence.

Implicit Culture for Multi-agent Interaction Support

Implicit Culture is the relation between a set and a group of agents such that the elements of the set behave according to the culture of the group. Earlier work claimed that supporting Implicit Culture phenomena can be useful in both artificial and human agents. In this paper, we recall the concept of Implicit Culture, present an implementation of a System for Implicit Culture Support (SICS) for multi-agent systems, and show how to use it for supporting agent interaction. We also present the application of the SICS to the eCulture Brokering System, a multi-agent system designed to mediate access to cultural information.

Collaborative Case-Based Recommender Systems

We introduce an application combining CBR and collaborative filtering techniques in the music domain. We describe a scenario in which a new kind of recommendation is required, which is capable of summarizing many recommendations in one suggestion. Our claim is that recommending one set of goods is different from recommending a single good many times. The paper illustrates how a case-based reasoning approach can provide an effective solution to this problem reducing the drawbacks related to the user profiles. CoCoA, a compilation compiler advisor, will be described as a running example of a collaborative case-based recommendation system.

Collaborative Radio Community

Recommender systems have been usually designed to support a single user in a one-to-one relation between a human and a service provider. This paper presents a collaborative radio community where the system delivers a personalization service on the fly, on the basis of the group recommending, promoting a shift from the one-to-one approach to a one-to-group scenario where the goal is assisting people in forming communities.

Information access in implicit culture framework

The goal of a System for Implicit Culture Support (SICS) is to establish an implicit culture phenomenon, namely when the elements of a set behave according to the culture of a generally different group of agents. Earlier work claimed that Implicit Culture support can be seen as a generalization of Collaborative Filtering. In this paper, we recall the concept of Implicit Culture, show how it is useful for automatically exploit tacit knowledge and we present an implementation of a System for Implicit Culture Support.