Search Results for author:
Found 9 papers, 4 papers with code
MIRACL-VISION: A Large, multilingual, visual document retrieval benchmark
MIRACL-VISION is a challenging, representative, multilingual evaluation benchmark for visual retrieval pipelines and will help the community build robust models for document retrieval.
Enhancing Q&A Text Retrieval with Ranking Models: Benchmarking, fine-tuning and deploying Rerankers for RAG
We focus on text retrieval for question-answering tasks, a common use case for Retrieval-Augmented Generation systems.
NV-Retriever: Improving text embedding models with effective hard-negative mining
One of the challenging aspects of fine-tuning embedding models is the selection of high quality hard-negative passages for contrastive learning.
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender Systems
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 retention and fairness, often taking a back seat.
EvalRS: a Rounded Evaluation of Recommender Systems
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 user interfaces.
Transformers with multi-modal features and post-fusion context for e-commerce session-based recommendation
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 sessions.
Hybrid Session-based News Recommendation using Recurrent Neural Networks
We describe a hybrid meta-architecture -- the CHAMELEON -- for session-based news recommendation that is able to leverage a variety of information types using Recurrent Neural Networks.
On the Importance of News Content Representation in Hybrid Neural Session-based Recommender Systems
A particular problem in that context is that online readers are often anonymous, which means that this personalization can only be based on the last few recorded interactions with the user, a setting named session-based recommendation.
News Session-Based Recommendations using Deep Neural Networks
This architecture is composed of two modules, the first responsible to learn news articles representations, based on their text and metadata, and the second module aimed to provide session-based recommendations using Recurrent Neural Networks.
