Graph learning based recommender systems: A review
Recent years have witnessed the fast development of the emerging topic of Graph Learning
based Recommender Systems (GLRS). GLRS employ advanced graph learning …
based Recommender Systems (GLRS). GLRS employ advanced graph learning …
A survey on session-based recommender systems
Recommender systems (RSs) have been playing an increasingly important role for informed
consumption, services, and decision-making in the overloaded information era and digitized …
consumption, services, and decision-making in the overloaded information era and digitized …
Tallrec: An effective and efficient tuning framework to align large language model with recommendation
Large Language Models (LLMs) have demonstrated remarkable performance across
diverse domains, thereby prompting researchers to explore their potential for use in …
diverse domains, thereby prompting researchers to explore their potential for use in …
Contrastive learning for sequential recommendation
Sequential recommendation methods play a crucial role in modern recommender systems
because of their ability to capture a user's dynamic interest from her/his historical inter …
because of their ability to capture a user's dynamic interest from her/his historical inter …
Global context enhanced graph neural networks for session-based recommendation
Session-based recommendation (SBR) is a challenging task, which aims at recommending
items based on anonymous behavior sequences. Almost all the existing solutions for SBR …
items based on anonymous behavior sequences. Almost all the existing solutions for SBR …
Cross-domain recommendation: challenges, progress, and prospects
To address the long-standing data sparsity problem in recommender systems (RSs), cross-
domain recommendation (CDR) has been proposed to leverage the relatively richer …
domain recommendation (CDR) has been proposed to leverage the relatively richer …
[PDF][PDF] Deep graph structure learning for robust representations: A survey
Abstract Graph Neural Networks (GNNs) are widely used for analyzing graph-structured
data. Most GNN methods are highly sensitive to the quality of graph structures and usually …
data. Most GNN methods are highly sensitive to the quality of graph structures and usually …
A bi-step grounding paradigm for large language models in recommendation systems
As the focus on Large Language Models (LLMs) in the field of recommendation intensifies,
the optimization of LLMs for recommendation purposes (referred to as LLM4Rec) assumes a …
the optimization of LLMs for recommendation purposes (referred to as LLM4Rec) assumes a …
ASTREAM: Data-stream-driven scalable anomaly detection with accuracy guarantee in IIoT environment
Intrusion detection exerts a crucial influence on securing the IIoT driven by anomaly
detection approaches. Dissimilar with the static data, the intrusion detection data is in the …
detection approaches. Dissimilar with the static data, the intrusion detection data is in the …
Popularity-aware and diverse web APIs recommendation based on correlation graph
The ever-increasing web application programming interfaces (APIs) in various service-
sharing communities (eg, ProgrammableWeb. com and Mashape. com) have enabled …
sharing communities (eg, ProgrammableWeb. com and Mashape. com) have enabled …