149 citations · 377 across the 14 of their papers we have counts for
14 papers
Justification of Recommender Systems Results: A Service-based Approach
Noemi Mauro, Zhongli Filippo Hu, Liliana Ardissono
With the increasing demand for predictable and accountable Artificial Intelligence, the ability to explain or justify recommender systems results by specifying how items are sugges…
Using consumer feedback from location-based services in PoI recommender systems for people with autism
Noemi Mauro, Liliana Ardissono, Stefano Cocomazzi +1
When suggesting Points of Interest (PoIs) to people with autism spectrum disorders, we must take into account that they have idiosyncratic sensory aversions to noise, brightness an…
User and Item-aware Estimation of Review Helpfulness
Noemi Mauro, Liliana Ardissono, Giovanna Petrone
In online review sites, the analysis of user feedback for assessing its helpfulness for decision-making is usually carried out by locally studying the properties of individual revi…
Session-aware Recommendation: A Surprising Quest for the State-of-the-art
Sara Latifi, Noemi Mauro, Dietmar Jannach
Recommender systems are designed to help users in situations of information overload. In recent years, we observed increased interest in session-based recommendation scenarios, whe…
Faceted Search of Heterogeneous Geographic Information for Dynamic Map Projection
Noemi Mauro, Liliana Ardissono, Maurizio Lucenteforte
This paper proposes a faceted information exploration model that supports coarse-grained and fine-grained focusing of geographic maps by offering a graphical representation of data…
Personalized Recommendation of PoIs to People with Autism
Noemi Mauro, Liliana Ardissono, Federica Cena
The suggestion of Points of Interest to people with Autism Spectrum Disorder (ASD) challenges recommender systems research because these users' perception of places is influenced b…
Impact of Semantic Granularity on Geographic Information Search Support
Noemi Mauro, Liliana Ardissono, Laura Di Rocco +2
The Information Retrieval research has used semantics to provide accurate search results, but the analysis of conceptual abstraction has mainly focused on information integration.…
Map-Based Visualization of 2D/3D Spatial Data via Stylization and Tuning of Information Emphasis
Liliana Ardissono, Matteo Delsanto, Maurizio Lucenteforte +3
In Geographical Information search, map visualization can challenge the user because results can consist of a large set of heterogeneous items, increasing visual complexity. We pro…
Concept-aware Geographic Information Retrieval
Noemi Mauro, Liliana Ardissono, Adriano Savoca
Textual queries are largely employed in information retrieval to let users specify search goals in a natural way. However, differences in user and system terminologies can challeng…
Extending a Tag-based Collaborative Recommender with Co-occurring Information Interests
Noemi Mauro, Liliana Ardissono
Collaborative Filtering is largely applied to personalize item recommendation but its performance is affected by the sparsity of rating data. In order to address this issue, recent…
Multi-faceted Trust-based Collaborative Filtering
Noemi Mauro, Liliana Ardissono, Zhongli Filippo Hu
Many collaborative recommender systems leverage social correlation theories to improve suggestion performance. However, they focus on explicit relations between users and they leav…
Session-based Suggestion of Topics for Geographic Exploratory Search
Noemi Mauro, Liliana Ardissono
Exploratory information search can challenge users in the formulation of efficacious search queries. Moreover, complex information spaces, such as those managed by Geographical Inf…
Empirical Analysis of Session-Based Recommendation Algorithms
Malte Ludewig, Noemi Mauro, Sara Latifi +1
Recommender systems are tools that support online users by pointing them to potential items of interest in situations of information overload. In recent years, the class of session…
A Compositional Model of Multi-faceted Trust for Personalized Item Recommendation
Liliana Ardissono, Noemi Mauro
Trust-based recommender systems improve rating prediction with respect to Collaborative Filtering by leveraging the additional information provided by a trust network among users t…