activity
20192022
most citedEmpirical Analysis of Session-Based Recommendation Algorithms

149 citations · 377 across the 14 of their papers we have counts for

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Showing 2020Show all

10 papers · 1 filter

cs.IR2020★ 31 cited

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…

cs.IR2020★ 67 cited

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…

cs.HC2020★ 19 cited

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…

cs.IR2020★ 20 cited

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…

cs.IR2020★ 4 cited

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.…

cs.HC2020★ 6 cited

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…

cs.IR2020★ 4 cited

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…

cs.IR2020★ 3 cited

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…

cs.IR2020★ 14 cited

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…

cs.IR2020★ 5 cited

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…