153 citations · 369 across the 18 of their papers we have counts for
15 papers · 1 filter
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…
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…
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…
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…
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…
Learning Early Exit Strategies for Additive Ranking Ensembles
Francesco Busolin, Claudio Lucchese, Franco Maria Nardini +3
Modern search engine ranking pipelines are commonly based on large machine-learned ensembles of regression trees. We propose LEAR, a novel - learned - technique aimed to reduce the…