Recommender systems in the era of large language models (llms)
With the prosperity of e-commerce and web applications, Recommender Systems (RecSys)
have become an important component of our daily life, providing personalized suggestions …
have become an important component of our daily life, providing personalized suggestions …
Recommender systems in the era of large language models (llms)
With the prosperity of e-commerce and web applications, Recommender Systems (RecSys)
have become an indispensable and important component in our daily lives, providing …
have become an indispensable and important component in our daily lives, providing …
Multi-task item-attribute graph pre-training for strict cold-start item recommendation
Recommendation systems suffer in the strict cold-start (SCS) scenario, where the user-item
interactions are entirely unavailable. The well-established, dominating identity (ID)-based …
interactions are entirely unavailable. The well-established, dominating identity (ID)-based …
MultiGPrompt for multi-task pre-training and prompting on graphs
Graph Neural Networks (GNNs) have emerged as a mainstream technique for graph
representation learning. However, their efficacy within an end-to-end supervised framework …
representation learning. However, their efficacy within an end-to-end supervised framework …
CIRP: Cross-Item Relational Pre-training for Multimodal Product Bundling
Product bundling has been a prevailing marketing strategy that is beneficial in the online
shopping scenario. Effective product bundling methods depend on high-quality item …
shopping scenario. Effective product bundling methods depend on high-quality item …
GACRec: Generative adversarial contrastive learning for improved long-tail item recommendation
B Qin, Z Huang, X Tian, Y Chen, W Wang - Knowledge-Based Systems, 2024 - Elsevier
The long-tail distribution of items is common in recommendation systems. However, due to
the limited interaction records of long-tail items, recommending them to users significantly …
the limited interaction records of long-tail items, recommending them to users significantly …
Scaling Sequential Recommendation Models with Transformers
Modeling user preferences has been mainly addressed by looking at users' interaction
history with the different elements available in the system. Tailoring content to individual …
history with the different elements available in the system. Tailoring content to individual …
Personalized Multi-task Training for Recommender System
In the vast landscape of internet information, recommender systems (RecSys) have become
essential for guiding users through a sea of choices aligned with their preferences. These …
essential for guiding users through a sea of choices aligned with their preferences. These …
Multi-Label Zero-Shot Product Attribute-Value Extraction
J Gong, H Eldardiry - Proceedings of the ACM on Web Conference 2024, 2024 - dl.acm.org
E-commerce platforms should provide detailed product descriptions (attribute values) for
effective product search and recommendation. However, attribute value information is …
effective product search and recommendation. However, attribute value information is …
A Survey of Reasoning for Substitution Relationships: Definitions, Methods, and Directions
A Yang, Z Du, T Sun - arXiv preprint arXiv:2404.08687, 2024 - arxiv.org
Substitute relationships are fundamental to people's daily lives across various domains. This
study aims to comprehend and predict substitute relationships among products in diverse …
study aims to comprehend and predict substitute relationships among products in diverse …