Results 21 to 30 of about 665 (142)

Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

, 2021
Social relations are often used to improve recommendation quality when user-item interaction data is sparse in recommender systems. Most existing social recommendation models exploit pairwise relations to mine potential user preferences.
Zhang, Xiangliang, Junliang Yu, Li, Jundong, Hongzhi Yin, Jundong Li, Nguyen Quoc Viet Hung, Hung, Nguyen Quoc Viet, Wang, Qinyong, Qinyong Wang, Yu, Junliang, Nguyen, Quoc Viet Hung, Yin, Hongzhi, Xiangliang Zhang   +12 more
core   +1 more source

Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation

, 2021
Session-based recommendation (SBR) focuses on next-item prediction at a certain time point. As user profiles are generally not available in this scenario, capturing the user intent lying in the item transitions plays a pivotal role.
Zhang, Xiangliang, Wang, Qinyong, Cui, Lizhen, Yu, Junliang, Yin, Hongzhi, Xia, Xin   +5 more
core   +2 more sources

A family of \(t\)-regular self-complementary \(k\)-hypergraphs

Transactions on Combinatorics, 2017
Summary: We use the recursive method of construction large sets of \(t\)-designs given by \textit{Q.-r. Wu} [Australas. J. Comb. 4, 229--235 (1991; Zbl 0763.05013)], and present a similar method for constructing \(t\)-subset-regular self-complementary \(k\)-uniform hypergraphs of order \(v\). As an application we show the existence of a new family of 2-
Ariannejad, Masoud, Emami, Mojgan, Naserian, Ozra   +2 more
openaire   +2 more sources

Spatial-Temporal Hypergraph Self-Supervised Learning for Crime Prediction

, 2022
Crime has become a major concern in many cities, which calls for the rising demand for timely predicting citywide crime occurrence. Accurate crime prediction results are vital for the beforehand decision-making of government to alleviate the increasing ...
Xu, Yong, Xia, Lianghao, Li, Zhonghang, Pei, Jian, Huang, Chao   +4 more
core   +2 more sources

Hypergraph Contrastive Collaborative Filtering

, 2022
Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing users and items into latent representation space, with their correlative patterns from interaction data.
Xu, Yong, Huang, C, Xu, Y, Xia, Lianghao, Xia, L, Zhao, J, Huang, J, Huang, Chao, Yin, Dawei, Yin, D, Huang, Jimmy, Zhao, Jiashu   +11 more
core   +1 more source

A note on 2-subset-regular self-complementary 3-uniform hypergraphs

Ars Comb., 2008
We show that a 2-subset-regular self-complementary 3-uniform hypergraph with $n$ vertices exists if and only if $n\ge 6$ and $n$ is congruent to 2 modulo 4.
Martin Knor, Primoz Potocnik
openaire   +2 more sources

Self-Supervised Hypergraph Transformer for Recommender Systems

, 2022
Graph Neural Networks (GNNs) have been shown as promising solutions for collaborative filtering (CF) with the modeling of user-item interaction graphs. The key idea of existing GNN-based recommender systems is to recursively perform the message passing ...
Lianghao Xia, Zhang, Chuxu, Chao Huang, Huang, C, Chuxu Zhang, Xia, Lianghao, Xia, L, Zhang, C, Huang, Chao   +8 more
core   +1 more source

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

Advanced Science, EarlyView.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu, Mengtao Sun, Lusheng Li, Jieqiong Wang, Shibiao Wan   +4 more
wiley   +1 more source

Topology‐Aware Deep Learning on Higher‐Order Structures for Drug Response Prediction

Advanced Science, EarlyView.
We present TopDr, a topology‐aware deep learning framework that encodes both drugs and cell lines as multiscale simplicial complexes, capturing interactions at the 0‐, 1‐, and 2‐simplex levels. By jointly integrating local higher‐order neighborhoods and global topological structures, TopDr generates enriched representations for sensitivity prediction ...
Cong Shen, Guancen Lin, Chuan‐Shen Hu, Yong Wang   +3 more
wiley   +1 more source

A Dynamic Correlation‐Information‐Fusion‐Based Spatiotemporal Network for Traffic Flow Forecasting

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT Traffic Flow Forecasting (TFF) is a foundational task in the development of Intelligent Transport Systems (ITSs). The primary challenge is to undertake a comprehensive exploration of the intrinsic dynamic spatiotemporal correlations of the road network, unveiling the long‐term evolutionary traffic trends.
Dawen Xia, Zhan Lin, Xingyan Wang, Ruixi Huang, Jinhui Hu, Yang Hu, Huaqing Li   +6 more
wiley   +1 more source