Search Results for author: Maria Maistro

Found 12 papers, 8 papers with code

Recommending Target Actions Outside Sessions in the Data-poor Insurance Domain

no code implementations1 Mar 2024 Simone Borg Bruun, Christina Lioma, Maria Maistro

Our models cope with data scarcity by learning from multiple sessions and different types of user actions.

Graph-based Recommendation for Sparse and Heterogeneous User Interactions

1 code implementation26 Jan 2023 Simone Borg Bruun, Kacper Kenji Lesniak, Mirko Biasini, Vittorio Carmignani, Panagiotis Filianos, Christina Lioma, Maria Maistro

We propose a graph-based recommender model which utilizes heterogeneous interactions between users and content of different types and is able to operate well on small-scale datasets.

Recommendation Systems

Principled Multi-Aspect Evaluation Measures of Rankings

1 code implementation1 Dec 2022 Maria Maistro, Lucas Chaves Lima, Jakob Grue Simonsen, Christina Lioma

Information Retrieval evaluation has traditionally focused on defining principled ways of assessing the relevance of a ranked list of documents with respect to a query.

Document Ranking Information Retrieval +1

Learning Recommendations from User Actions in the Item-poor Insurance Domain

1 code implementation28 Nov 2022 Simone Borg Bruun, Maria Maistro, Christina Lioma

To address this, we present a recurrent neural network recommendation model that uses past user sessions as signals for learning recommendations.

Session-Based Recommendations

repro_eval: A Python Interface to Reproducibility Measures of System-oriented IR Experiments

1 code implementation19 Jan 2022 Timo Breuer, Nicola Ferro, Maria Maistro, Philipp Schaer

In this work we introduce repro_eval - a tool for reactive reproducibility studies of system-oriented information retrieval (IR) experiments.

Information Retrieval Retrieval

University of Copenhagen Participation in TREC Health Misinformation Track 2020

no code implementations3 Mar 2021 Lucas Chaves Lima, Dustin Brandon Wright, Isabelle Augenstein, Maria Maistro

Our approach consists of 3 steps: (1) we create an initial run with BM25 and RM3; (2) we estimate credibility and misinformation scores for the documents in the initial run; (3) we merge the relevance, credibility and misinformation scores to re-rank documents in the initial run.

Language Modelling Misinformation +1

Multi-Head Self-Attention with Role-Guided Masks

1 code implementation22 Dec 2020 Dongsheng Wang, Casper Hansen, Lucas Chaves Lima, Christian Hansen, Maria Maistro, Jakob Grue Simonsen, Christina Lioma

The state of the art in learning meaningful semantic representations of words is the Transformer model and its attention mechanisms.

Machine Translation text-classification +2

Denmark's Participation in the Search Engine TREC COVID-19 Challenge: Lessons Learned about Searching for Precise Biomedical Scientific Information on COVID-19

no code implementations25 Nov 2020 Lucas Chaves Lima, Casper Hansen, Christian Hansen, Dongsheng Wang, Maria Maistro, Birger Larsen, Jakob Grue Simonsen, Christina Lioma

This report describes the participation of two Danish universities, University of Copenhagen and Aalborg University, in the international search engine competition on COVID-19 (the 2020 TREC-COVID Challenge) organised by the U. S. National Institute of Standards and Technology (NIST) and its Text Retrieval Conference (TREC) division.

Retrieval Text Retrieval

How to Measure the Reproducibility of System-oriented IR Experiments

1 code implementation26 Oct 2020 Timo Breuer, Nicola Ferro, Norbert Fuhr, Maria Maistro, Tetsuya Sakai, Philipp Schaer, Ian Soboroff

Replicability and reproducibility of experimental results are primary concerns in all the areas of science and IR is not an exception.

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