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Found 6 papers, 2 papers with code
Learning to Embed Multi-Modal Contexts for Situated Conversational Agents
The Situated Interactive Multi-Modal Conversations (SIMMC) 2. 0 aims to create virtual shopping assistants that can accept complex multi-modal inputs, i. e. visual appearances of objects and user utterances.
Ranked #2 on
Response Generation
on SIMMC2.0
IRA: Adaptive Interest-aware Representation and Alignment for Personalized Multi-interest Retrieval
Online community platforms require dynamic personalized retrieval and recommendation that can continuously adapt to evolving user interests and new documents.
RRADistill: Distilling LLMs' Passage Ranking Ability for Long-Tail Queries Document Re-Ranking on a Search Engine
Large Language Models (LLMs) excel at understanding the semantic relationships between queries and documents, even with lengthy and complex long-tail queries.
MvFS: Multi-view Feature Selection for Recommender System
Feature selection, which is a technique to select key features in recommender systems, has received increasing research attention.
Augment & Valuate : A Data Enhancement Pipeline for Data-Centric AI
For this reason, data-centric approaches are crucial for the automation of machine learning operation pipeline.
Dual Correction Strategy for Ranking Distillation in Top-N Recommender System
Knowledge Distillation (KD), which transfers the knowledge of a well-trained large model (teacher) to a small model (student), has become an important area of research for practical deployment of recommender systems.
