activity
20042023
most citedCoPhIR: a Test Collection for Content-Based Image Retrieval

153 citations · 369 across the 18 of their papers we have counts for

collaborators

20 papers

cs.IR2023

SE-PEF: a Resource for Personalized Expert Finding

Pranav Kasela, Gabriella Pasi, Raffaele Perego

The problem of personalization in Information Retrieval has been under study for a long time. A well-known issue related to this task is the lack of publicly available datasets tha…

cs.IR2023★ 1 cited

Post-hoc Selection of Pareto-Optimal Solutions in Search and Recommendation

Vincenzo Paparella, Vito Walter Anelli, Franco Maria Nardini +2

Information Retrieval (IR) and Recommender Systems (RS) tasks are moving from computing a ranking of final results based on a single metric to multi-objective problems. Solving the…

cs.IR2023★ 10 cited

SE-PQA: Personalized Community Question Answering

Pranav Kasela, Marco Braga, Gabriella Pasi +1

Personalization in Information Retrieval is a topic studied for a long time. Nevertheless, there is still a lack of high-quality, real-world datasets to conduct large-scale experim…

cs.IR2022

Caching Historical Embeddings in Conversational Search

Ophir Frieder, Ida Mele, Cristina Ioana Muntean +3

Rapid response, namely low latency, is fundamental in search applications; it is particularly so in interactive search sessions, such as those encountered in conversational setting…

cs.SI2022

Social Search: retrieving information in Online Social Platforms -- A Survey

Maddalena Amendola, Andrea Passarella, Raffaele Perego

Social Search research deals with studying methodologies exploiting social information to better satisfy user information needs in Online Social Media while simplifying the search…

cs.IR2022★ 7 cited

ILMART: Interpretable Ranking with Constrained LambdaMART

Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando +2

Interpretable Learning to Rank (LtR) is an emerging field within the research area of explainable AI, aiming at developing intelligible and accurate predictive models. While most o…