When large language models meet personalization: Perspectives of challenges and opportunities
The advent of large language models marks a revolutionary breakthrough in artificial
intelligence. With the unprecedented scale of training and model parameters, the capability …
intelligence. With the unprecedented scale of training and model parameters, the capability …
Multimodal graph contrastive learning for multimedia-based recommendation
Multimedia-based recommendation is a challenging task that requires not only learning
collaborative signals from user-item interaction, but also capturing modality-specific user …
collaborative signals from user-item interaction, but also capturing modality-specific user …
Recommendation systems: An insight into current development and future research challenges
Research on recommendation systems is swiftly producing an abundance of novel methods,
constantly challenging the current state-of-the-art. Inspired by advancements in many …
constantly challenging the current state-of-the-art. Inspired by advancements in many …
Multimodal recommender systems: A survey
The recommender system (RS) has been an integral toolkit of online services. They are
equipped with various deep learning techniques to model user preference based on …
equipped with various deep learning techniques to model user preference based on …
Multi-scale broad collaborative filtering for personalized recommendation
Y Gao, ZW Huang, ZY Huang, L Huang, Y Kuang… - Knowledge-based …, 2023 - Elsevier
Recently, neighborhood-based collaborative filtering has been increasingly used in
personalized recommender systems. However, inevitably, the neighborhood selection is …
personalized recommender systems. However, inevitably, the neighborhood selection is …
Shilling black-box review-based recommender systems through fake review generation
Review-Based Recommender Systems (RBRS) have attracted increasing research interest
due to their ability to alleviate well-known cold-start problems. RBRS utilizes reviews to …
due to their ability to alleviate well-known cold-start problems. RBRS utilizes reviews to …
RI-GCN: Review-aware interactive graph convolutional network for review-based item recommendation
Y Cai, Y Wang, W Wang, W Chen - 2022 IEEE International …, 2022 - ieeexplore.ieee.org
A wealth of semantic features exist in the reviews written by users, such as rich information
on item features and implicit preferences of users. Existing review-based recommendation …
on item features and implicit preferences of users. Existing review-based recommendation …
Set-sequence-graph: A multi-view approach towards exploiting reviews for recommendation
Existing review-based recommendation models mainly learn long-term user and item
representations from a set of reviews. Due to the ignorance of rich side information of …
representations from a set of reviews. Due to the ignorance of rich side information of …
Unsupervised extractive summarization-based representations for accurate and explainable collaborative filtering
We pioneer the first extractive summarization-based collaborative filtering model called
ESCOFILT. Our proposed model specifically produces extractive summaries for each item …
ESCOFILT. Our proposed model specifically produces extractive summaries for each item …
Learning hierarchical review graph representations for recommendation
The user review data have been demonstrated to be effective in solving different
recommendation problems. Previous review-based recommendation methods usually …
recommendation problems. Previous review-based recommendation methods usually …