[HTML][HTML] Economic recommender systems–a systematic review
Many of today's online services provide personalized recommendations to their users. Such
recommendations are typically designed to serve certain user needs, eg, to quickly find …
recommendations are typically designed to serve certain user needs, eg, to quickly find …
Graph and sequential neural networks in session-based recommendation: A survey
Recent years have witnessed the remarkable success of recommendation systems (RSs) in
alleviating the information overload problem. As a new paradigm of RSs, session-based …
alleviating the information overload problem. As a new paradigm of RSs, session-based …
Denoising and prompt-tuning for multi-behavior recommendation
In practical recommendation scenarios, users often interact with items under multi-typed
behaviors (eg, click, add-to-cart, and purchase). Traditional collaborative filtering techniques …
behaviors (eg, click, add-to-cart, and purchase). Traditional collaborative filtering techniques …
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 …
Homophily-oriented heterogeneous graph rewiring
With the rapid development of the World Wide Web (WWW), heterogeneous graphs (HG)
have explosive growth. Recently, heterogeneous graph neural network (HGNN) has shown …
have explosive growth. Recently, heterogeneous graph neural network (HGNN) has shown …
Large language models for intent-driven session recommendations
The goal of intent-aware session recommendation (ISR) approaches is to capture user
intents within a session for accurate next-item prediction. However, the capability of these …
intents within a session for accurate next-item prediction. However, the capability of these …
Cross-view hypergraph contrastive learning for attribute-aware recommendation
Recommender systems typically model user–item interaction data to learn user interests and
preferences. However, user interactions are often sparse and noisy. Moreover, existing …
preferences. However, user interactions are often sparse and noisy. Moreover, existing …
Knowledge-enhanced multi-view graph neural networks for session-based recommendation
Q Chen, Z Guo, J Li, G Li - Proceedings of the 46th international ACM …, 2023 - dl.acm.org
Session-based recommendation (SBR) has received increasing attention to predict the next
item via extracting and integrating both global and local item-item relationships. However …
item via extracting and integrating both global and local item-item relationships. However …
Integrating user short-term intentions and long-term preferences in heterogeneous hypergraph networks for sequential recommendation
Sequential recommendation tries to model the binary correlations among users and items in
a sequence to provide accurate recommendations. However, user behaviors are influenced …
a sequence to provide accurate recommendations. However, user behaviors are influenced …
Enhancing collaborative information with contrastive learning for session-based recommendation
G An, J Sun, Y Yang, F Sun - Information Processing & Management, 2024 - Elsevier
Session-based recommendation (SBR) aims to exploit the session representation generated
by combining item embedding and session embedding processes to recommend the next …
by combining item embedding and session embedding processes to recommend the next …