Pedestrian-specific bipartite-aware similarity learning for text-based person retrieval
Text-based person retrieval is a challenging task that aims to search pedestrian images with
the same identity according to language descriptions. Current methods usually …
the same identity according to language descriptions. Current methods usually …
Aligning distillation for cold-start item recommendation
Recommending cold items in recommendation systems is a longstanding challenge due to
the inherent differences between warm items, which are recommended based on user …
the inherent differences between warm items, which are recommended based on user …
Contrastive learning for cold-start recommendation
Recommending purely cold-start items is a long-standing and fundamental challenge in the
recommender systems. Without any historical interaction on cold-start items, the …
recommender systems. Without any historical interaction on cold-start items, the …
Multi-modal graph contrastive learning for micro-video recommendation
Recently micro-videos have become more popular in social media platforms such as TikTok
and Instagram. Engagements in these platforms are facilitated by multi-modal …
and Instagram. Engagements in these platforms are facilitated by multi-modal …
Multimodal pretraining, adaptation, and generation for recommendation: A survey
Personalized recommendation serves as a ubiquitous channel for users to discover
information tailored to their interests. However, traditional recommendation models primarily …
information tailored to their interests. However, traditional recommendation models primarily …
Missrec: Pre-training and transferring multi-modal interest-aware sequence representation for recommendation
The goal of sequential recommendation (SR) is to predict a user's potential interested items
based on her/his historical interaction sequences. Most existing sequential recommenders …
based on her/his historical interaction sequences. Most existing sequential recommenders …
Generative adversarial framework for cold-start item recommendation
The cold-start problem has been a long-standing issue in recommendation. Embedding-
based recommendation models provide recommendations by learning embeddings for each …
based recommendation models provide recommendations by learning embeddings for each …
Contrastive collaborative filtering for cold-start item recommendation
The cold-start problem is a long-standing challenge in recommender systems. As a
promising solution, content-based generative models usually project a cold-start item's …
promising solution, content-based generative models usually project a cold-start item's …
LGMRec: local and global graph learning for multimodal recommendation
Z Guo, J Li, G Li, C Wang, S Shi, B Ruan - Proceedings of the AAAI …, 2024 - ojs.aaai.org
The multimodal recommendation has gradually become the infrastructure of online media
platforms, enabling them to provide personalized service to users through a joint modeling …
platforms, enabling them to provide personalized service to users through a joint modeling …
Multimedia recommender systems: Algorithms and challenges
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 …
(MMRS), focusing on methods that integrate multimedia content as side information to …