1,488 research outputs found
Session-based Recommendation with Graph Neural Networks
The problem of session-based recommendation aims to predict user actions
based on anonymous sessions. Previous methods model a session as a sequence and
estimate user representations besides item representations to make
recommendations. Though achieved promising results, they are insufficient to
obtain accurate user vectors in sessions and neglect complex transitions of
items. To obtain accurate item embedding and take complex transitions of items
into account, we propose a novel method, i.e. Session-based Recommendation with
Graph Neural Networks, SR-GNN for brevity. In the proposed method, session
sequences are modeled as graph-structured data. Based on the session graph, GNN
can capture complex transitions of items, which are difficult to be revealed by
previous conventional sequential methods. Each session is then represented as
the composition of the global preference and the current interest of that
session using an attention network. Extensive experiments conducted on two real
datasets show that SR-GNN evidently outperforms the state-of-the-art
session-based recommendation methods consistently.Comment: 9 pages, 4 figures, accepted by AAAI Conference on Artificial
Intelligence (AAAI-19
CARPe Posterum: A Convolutional Approach for Real-time Pedestrian Path Prediction
Pedestrian path prediction is an essential topic in computer vision and video
understanding. Having insight into the movement of pedestrians is crucial for
ensuring safe operation in a variety of applications including autonomous
vehicles, social robots, and environmental monitoring. Current works in this
area utilize complex generative or recurrent methods to capture many possible
futures. However, despite the inherent real-time nature of predicting future
paths, little work has been done to explore accurate and computationally
efficient approaches for this task. To this end, we propose a convolutional
approach for real-time pedestrian path prediction, CARPe. It utilizes a
variation of Graph Isomorphism Networks in combination with an agile
convolutional neural network design to form a fast and accurate path prediction
approach. Notable results in both inference speed and prediction accuracy are
achieved, improving FPS considerably in comparison to current state-of-the-art
methods while delivering competitive accuracy on well-known path prediction
datasets.Comment: AAAI-21 Camera Read
Class-Attentive Diffusion Network for Semi-Supervised Classification
Recently, graph neural networks for semi-supervised classification have been
widely studied. However, existing methods only use the information of limited
neighbors and do not deal with the inter-class connections in graphs. In this
paper, we propose Adaptive aggregation with Class-Attentive Diffusion (AdaCAD),
a new aggregation scheme that adaptively aggregates nodes probably of the same
class among K-hop neighbors. To this end, we first propose a novel stochastic
process, called Class-Attentive Diffusion (CAD), that strengthens attention to
intra-class nodes and attenuates attention to inter-class nodes. In contrast to
the existing diffusion methods with a transition matrix determined solely by
the graph structure, CAD considers both the node features and the graph
structure with the design of our class-attentive transition matrix that
utilizes a classifier. Then, we further propose an adaptive update scheme that
leverages different reflection ratios of the diffusion result for each node
depending on the local class-context. As the main advantage, AdaCAD alleviates
the problem of undesired mixing of inter-class features caused by discrepancies
between node labels and the graph topology. Built on AdaCAD, we construct a
simple model called Class-Attentive Diffusion Network (CAD-Net). Extensive
experiments on seven benchmark datasets consistently demonstrate the efficacy
of the proposed method and our CAD-Net significantly outperforms the
state-of-the-art methods. Code is available at
https://github.com/ljin0429/CAD-Net.Comment: Accepted to AAAI 202
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