36 research outputs found
Graph-based Village Level Poverty Identification
Poverty status identification is the first obstacle to eradicating poverty.
Village-level poverty identification is very challenging due to the arduous
field investigation and insufficient information. The development of the Web
infrastructure and its modeling tools provides fresh approaches to identifying
poor villages. Upon those techniques, we build a village graph for village
poverty status identification. By modeling the village connections as a graph
through the geographic distance, we show the correlation between village
poverty status and its graph topological position and identify two key factors
(Centrality, Homophily Decaying effect) for identifying villages. We further
propose the first graph-based method to identify poor villages. It includes a
global Centrality2Vec module to embed village centrality into the dense vector
and a local graph distance convolution module that captures the decaying
effect. In this paper, we make the first attempt to interpret and identify
village-level poverty from a graph perspective.Comment: 5 pages, accepted by theWebConf 202
Graph-based Alignment and Uniformity for Recommendation
Collaborative filtering-based recommender systems (RecSys) rely on learning
representations for users and items to predict preferences accurately.
Representation learning on the hypersphere is a promising approach due to its
desirable properties, such as alignment and uniformity. However, the sparsity
issue arises when it encounters RecSys. To address this issue, we propose a
novel approach, graph-based alignment and uniformity (GraphAU), that explicitly
considers high-order connectivities in the user-item bipartite graph. GraphAU
aligns the user/item embedding to the dense vector representations of
high-order neighbors using a neighborhood aggregator, eliminating the need to
compute the burdensome alignment to high-order neighborhoods individually. To
address the discrepancy in alignment losses, GraphAU includes a layer-wise
alignment pooling module to integrate alignment losses layer-wise. Experiments
on four datasets show that GraphAU significantly alleviates the sparsity issue
and achieves state-of-the-art performance. We open-source GraphAU at
https://github.com/YangLiangwei/GraphAU.Comment: 4 page
DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation
Graph Neural Network (GNN) based recommender systems have been attracting
more and more attention in recent years due to their excellent performance in
accuracy. Representing user-item interactions as a bipartite graph, a GNN model
generates user and item representations by aggregating embeddings of their
neighbors. However, such an aggregation procedure often accumulates information
purely based on the graph structure, overlooking the redundancy of the
aggregated neighbors and resulting in poor diversity of the recommended list.
In this paper, we propose diversifying GNN-based recommender systems by
directly improving the embedding generation procedure. Particularly, we utilize
the following three modules: submodular neighbor selection to find a subset of
diverse neighbors to aggregate for each GNN node, layer attention to assign
attention weights for each layer, and loss reweighting to focus on the learning
of items belonging to long-tail categories. Blending the three modules into
GNN, we present DGRec(Diversified GNN-based Recommender System) for diversified
recommendation. Experiments on real-world datasets demonstrate that the
proposed method can achieve the best diversity while keeping the accuracy
comparable to state-of-the-art GNN-based recommender systems.Comment: 9 pages, WSDM 202
Contextual Collaboration: Uniting Collaborative Filtering with Pre-trained Language Models
Traditional recommender systems have predominantly relied on identity
representations (IDs) to characterize users and items. In contrast, the
emergence of pre-trained language model (PLM) en-coders has significantly
enriched the modeling of contextual item descriptions. While PLMs excel in
addressing few-shot, zero-shot, and unified modeling scenarios, they often
overlook the critical collaborative filtering signal. This omission gives rise
to two pivotal challenges: (1) Collaborative Contextualization, aiming for the
seamless integration of collaborative signals with contextual representations.
(2) The necessity to bridge the representation gap between ID-based and
contextual representations while preserving their contextual semantics. In this
paper, we introduce CollabContext, a novel model that skillfully merges
collaborative filtering signals with contextual representations, aligning these
representations within the contextual space while retaining essential
contextual semantics. Experimental results across three real-world datasets
showcase substantial improvements. Through its capability in collaborative
contextualization, CollabContext demonstrates remarkable enhancements in
recommendation performance, particularly in cold-start scenarios. The code is
available after the conference accepts the paper
Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering
Collaborative filtering (CF) is a widely employed technique that predicts
user preferences based on past interactions. Negative sampling plays a vital
role in training CF-based models with implicit feedback. In this paper, we
propose a novel perspective based on the sampling area to revisit existing
sampling methods. We point out that current sampling methods mainly focus on
Point-wise or Line-wise sampling, lacking flexibility and leaving a significant
portion of the hard sampling area un-explored. To address this limitation, we
propose Dimension Independent Mixup for Hard Negative Sampling (DINS), which is
the first Area-wise sampling method for training CF-based models. DINS
comprises three modules: Hard Boundary Definition, Dimension Independent Mixup,
and Multi-hop Pooling. Experiments with real-world datasets on both matrix
factorization and graph-based models demonstrate that DINS outperforms other
negative sampling methods, establishing its effectiveness and superiority. Our
work contributes a new perspective, introduces Area-wise sampling, and presents
DINS as a novel approach that achieves state-of-the-art performance for
negative sampling. Our implementations are available in PyTorch
Group Identification via Transitional Hypergraph Convolution with Cross-view Self-supervised Learning
With the proliferation of social media, a growing number of users search for
and join group activities in their daily life. This develops a need for the
study on the group identification (GI) task, i.e., recommending groups to
users. The major challenge in this task is how to predict users' preferences
for groups based on not only previous group participation of users but also
users' interests in items. Although recent developments in Graph Neural
Networks (GNNs) accomplish embedding multiple types of objects in graph-based
recommender systems, they, however, fail to address this GI problem
comprehensively. In this paper, we propose a novel framework named Group
Identification via Transitional Hypergraph Convolution with Graph
Self-supervised Learning (GTGS). We devise a novel transitional hypergraph
convolution layer to leverage users' preferences for items as prior knowledge
when seeking their group preferences. To construct comprehensive user/group
representations for GI task, we design the cross-view self-supervised learning
to encourage the intrinsic consistency between item and group preferences for
each user, and the group-based regularization to enhance the distinction among
group embeddings. Experimental results on three benchmark datasets verify the
superiority of GTGS. Additional detailed investigations are conducted to
demonstrate the effectiveness of the proposed framework.Comment: 11 pages. Accepted by CIKM'2
Case Report: Using Medtronic AP360 mechanical prosthesis in mitral valve replacement for patients with mitral insufficiency after primum atrial septal defect repair to reduce left ventricular outflow tract obstruction risk
BackgroundAtrial septal defect is one of the most common congenital heart diseases in adults. Primum atrial septal defect (PASD) accounts for 4%–5% of congenital heart defects. Patients with PASD frequently suffer mitral insufficiency (MI), and thus, mitral valvuloplasty (MVP) or mitral valve replacement (MVR) is often required at the time of PASD repair. Unfortunately, recurrent unrepairable severe mitral regurgitation can develop in many patients undergoing PASD repair plus MVP in either short- or long-term after the repair surgery, requiring a re-do MVR. In those patients, the risk of left ventricular outflow tract obstruction (LVOTO) has increased.Case presentationWe present five such cases, ranging in age from 24 to 47 years, who had a PASD repair plus MVP or MVR for 14–40 years while suffering moderate to severe mitral regurgitation. Using Medtronic AP360 mechanical mitral prostheses, only one patient experienced mild LVOTO.ConclusionsThe use of Medtronic AP360 mechanical mitral prostheses to perform MVR in patients with MI who had a history of PASD repair can potentially reduce the risk of LVOTO. Long-term follow-up is required to further confirm this clinical benefit associated with AP360 implantation in patients with PASD