Valentina Maccatrozzo

A Semantic Pattern-Based Recommender

This paper presents a novel approach for Linked Data-based recommender systems through the use of semantic patterns - generalized paths in a graph described through the types of the nodes and links involved. We apply this novel approach to the book dataset from the ESWC2014 recommender systems challenge. User profiles are built by aggregating ratings on patterns with respect to each book in provided user training set. Ratings are aggregated by estimating the expected value of a Beta distribution describing the rating given to each individual book. Our approach allows the determination of a rating for a book, even if the book is poorly connected with user profile. It allows for a “prudent” estimation thanks to smoothing. However, if many patterns are available, it considers all the contributions. Additionally, it allows for a lightweight computation of ratings as it exploits the knowledge encoded in the patterns. Our approach achieved a precision of 0.60 and an overall F-measure of about 0.52 on the ESWC2014 challenge.

Combining User Reputation and Provenance Analysis for Trust Assessment

Trust is a broad concept that in many systems is often reduced to user reputation alone. However, user reputation is just one way to determine trust. The estimation of trust can be tackled from other perspectives as well, including by looking at provenance. Here, we present a complete pipeline for estimating the trustworthiness of artifacts given their provenance and a set of sample evaluations. The pipeline is composed of a series of algorithms for (1) extracting relevant provenance features, (2) generating stereotypes of user behavior from provenance features, (3) estimating the reputation of both stereotypes and users, (4) using a combination of user and stereotype reputations to estimate the trustworthiness of artifacts, and (5) selecting sets of artifacts to trust. These algorithms rely on the W3C PROV recommendations for provenance and on evidential reasoning by means of subjective logic. We evaluate the pipeline over two tagging datasets: tags and evaluations from the Netherlands Institute for Sound and Vision’s Waisda? video tagging platform, as well as crowdsourced annotations from the Steve.Museum project. The approach achieves up to 85% precision when predicting tag trustworthiness. Perhaps more importantly, the pipeline provides satisfactory results using relatively little evidence through the use of provenance.

Two Procedures for Analyzing the Reliability of Open Government Data

Open Government Data often contain information that, in more or less detail, regard private citizens. For this reason, before publishing them, public authorities manipulate data to remove any sensitive information while trying to preserve their reliability. This paper addresses the lack of tools aimed at measuring the reliability of these data. We present two procedures for the assessment of the Open Government Data reliability, one based on a comparison between open and closed data, and the other based on analysis of open data only. We evaluate the procedures over data from the data.police.uk website and from the Hampshire Police Constabulary in the United Kingdom. The procedures effectively allow estimating the reliability of open data and, actually, their reliability is high even though they are aggregated and smoothed.

SIRUP: Serendipity In Recommendations via User Perceptions

In this paper, we propose a model to operationalise serendipity in content-based recommender systems. The model, called SIRUP, is inspired by the Silvias curiosity theory, based on the fundamental theory of Berlyne, aims at (1) measuring the novelty of an item with respect to the user profile, and (2) assessing whether the user is able to manage such level of novelty (coping potential). The novelty of items is calculated with cosine similarities between items, using Linked Open Data paths. The coping potential of users is estimated by measuring the diversity of the items in the user profile. We deployed and evaluated the SIRUP model in a use case with TV recommender using BBC programs dataset. Results show that the SIRUP model allows us to identify serendipitous recommendations, and, at the same time, to have 71% precision.