110 citations · 226 across the 8 of their papers we have counts for
8 papers · 1 filter
Beyond Semantic Similarity: Performance and Costs of Agentic Retrieval for Complex Tasks
Reza Esfandiarpoor, Radek Osmulski, Yauhen Babakhin +8
Modern information systems, including many agentic workflows, use dense retrieval to explore large amounts of unstructured data. However, dense retrieval relies on surface-level se…
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender Systems
Patrick John Chia, Giuseppe Attanasio, Jacopo Tagliabue +5
Recommender Systems today are still mostly evaluated in terms of accuracy, with other aspects beyond the immediate relevance of recommendations, such as diversity, long-term user r…
EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deployments
Federico Bianchi, Patrick John Chia, Ciro Greco +5
EvalRS aims to bring together practitioners from industry and academia to foster a debate on rounded evaluation of recommender systems, with a focus on real-world impact across a m…
EvalRS: a Rounded Evaluation of Recommender Systems
Jacopo Tagliabue, Federico Bianchi, Tobias Schnabel +4
Much of the complexity of Recommender Systems (RSs) comes from the fact that they are used as part of more complex applications and affect user experience through a varied range of…
Transformers with multi-modal features and post-fusion context for e-commerce session-based recommendation
Gabriel de Souza P. Moreira, Sara Rabhi, Ronay Ak +2
Session-based recommendation is an important task for e-commerce services, where a large number of users browse anonymously or may have very distinct interests for different sessio…
On the Importance of News Content Representation in Hybrid Neural Session-based Recommender Systems
Gabriel de Souza P. Moreira, Dietmar Jannach, Adilson Marques da Cunha
News recommender systems are designed to surface relevant information for online readers by personalizing their user experiences. A particular problem in that context is that onlin…
Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks
Gabriel de Souza Pereira Moreira, Dietmar Jannach, Adilson Marques da Cunha
Recommender systems help users deal with information overload by providing tailored item suggestions to them. The recommendation of news is often considered to be challenging, sinc…
News Session-Based Recommendations using Deep Neural Networks
Gabriel de Souza P. Moreira, Felipe Ferreira, Adilson Marques da Cunha
News recommender systems are aimed to personalize users experiences and help them to discover relevant articles from a large and dynamic search space. Therefore, news domain is a c…