The textile and apparel industries have grown tremendously over the past few years.
Customers no longer have to visit many stores, stand in long queues, or try on garments in …

This chapter studies state-of-the-art research related to multimedia recommender systems
(MMRS), focusing on methods that integrate multimedia content as side information to …

In sequential recommendation, multi-modal information (eg, text or image) can provide a
more comprehensive view of an item's profile. The optimal stage (early or late) to fuse …

Recent research on conversational information seeking (CIS) mostly focuses on uni-modal
interactions and information items. This per-spective paper highlights the importance of …

Incorporating deep learning models has marked a significant advancement in integrating
trends and technology within the fashion industry. These models are extensively applied in …

Generally, items with missing modalities are dropped in multimodal recommendation.
However, with this work, we question this procedure, highlighting that it would further …

Recommender Systems (RS) are based on the generalization of the observed interactions of
a population of users with a collection of items. Collaborative Filters (CF) give good results …

In fashion e-commerce, predicting item compatibility using visual features remains a
significant challenge. Current recommendation systems often struggle to incorporate high …

The paper introduces Visual-Elliot (V-Elliot), a reproducibility framework for Visual
Recommendation systems (VRSs) based on Elliot. framework provides the widest set of …

When customers' choices may depend on the visual appearance of products (eg, fashion),
visually-aware recommender systems (VRSs) have been shown to provide more accurate …