Lianghao Xia

Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation

2021 · arXiv (Cornell University)

Session-based recommendation plays a central role in a wide spectrum of online applications, ranging from e-commerce to online advertising services. However, the majority of existing session-based recommendation techniques (e.g., attention-based recurrent network or graph neural …

Graph Meta Network for Multi-Behavior Recommendation

2021

Modern recommender systems often embed users and items into low-dimensional latent representations, based on their observed interactions. In practical recommendation scenarios, users often exhibit various intents which drive them to interact with items with multiple …

Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation

2021 · Proceedings of the AAAI Conference on Artificial Intelligence

Accurate user and item embedding learning is crucial for modern recommender systems. However, most existing recommendation techniques have thus far focused on modeling users' preferences over singular type of user-item interactions. Many practical recommendation scenarios …

Automated Self-Supervised Learning for Recommendation

2023

Graph neural networks (GNNs) have emerged as the state-of-the-art paradigm for collaborative filtering (CF). To improve the representation quality over limited labeled data, contrastive learning has attracted attention in recommendation and benefited graph-based CF model …

Disentangled Graph Social Recommendation

2023

Social recommender systems have drawn a lot of attention in many online web services, because of the incorporation of social information between users in improving recommendation results. Despite the significant progress made by existing solutions, …

Knowledge-aware Coupled Graph Neural Network for Social Recommendation

2021 · Proceedings of the AAAI Conference on Artificial Intelligence

Social recommendation task aims to predict users' preferences over items with the incorporation of social connections among users, so as to alleviate the sparse issue of collaborative filtering. While many recent efforts show the effectiveness …

Hypergraph Contrastive Collaborative Filtering

2022 · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing users and items into latent representation space, with their correlative patterns from interaction data. Among various CF techniques, the development of GNN-based recommender systems, e.g., …

Knowledge Graph Contrastive Learning for Recommendation

2022 · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Knowledge Graphs (KGs) have been utilized as useful side information to improve recommendation quality. In those recommender systems, knowledge graph information often contains fruitful facts and inherent semantic relatedness among items. However, the success of …