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Linear Sequential Recommendation Models using Textual Side Information
Dongcheol Lee, Minjin Choi, Jongwuk Lee
http://doi.org/10.5626/JOK.2025.52.6.529
Recently, research on leveraging auxiliary information in sequential recommendation systems is being actively conducted. Most approaches have focused on combining language models with deep neural networks. However, they often lead to high computational costs and latency issues. While linear recommendation models can serve as an efficient alternative, research on how to effectively incorporate auxiliary information is lacking. This study proposed a framework that could effectively utilize auxiliary information within a linear model. Since textual data cannot be directly used in linear model training, we transformed item texts into dense vectors using a pre-trained text encoder. Although these vectors contained rich information, they failed to capture relationships between items. To address this, we applied graph convolution to obtain enhanced item representations. These representations were then used alongside the user-item interaction matrix for linear model training. Extensive experiments showed that the proposed method improved the overall performance, particularly in recommending less popular items.
Integrating Domain Knowledge with Graph Convolution based on a Semantic Network for Elderly Depression Prediction
Seok-Jun Bu, Kyoung-Won Park, Sung-Bae Cho
http://doi.org/10.5626/JOK.2023.50.3.243
Depression in the elderly is a global problem that causes 300 million patients and 800,000 suicides every year, so it is critical to detect early daily activity patterns closely related to mobility. Although a graph-convolution neural network based on sensing logs has been promising, it is required to represent high-level behaviors extracted from complex sensing information sequences. In this paper, a semantic network that structuralizes the daily activity patterns of the elderly was constructed using additional domain knowledge, and a graph convolution model was proposed for complementary uses of low-level sensing log graphs. Cross-validation with 800 hours of data from 69 senior citizens provided by DNX, Inc. revealed improved prediction performance for the suggested strategy compared to the most recent deep learning model. In particular, the inference of a semantic network was justified by a graph convolution model by showing a performance improvement of 28.86% compared with the conventional model.
Graph Convolutional Networks with Elaborate Neighborhood Selection
Yeonsung Jung, Joyce Jiyoung Whang
http://doi.org/10.5626/JOK.2019.46.11.1193
Graph Convolutional Networks (GCNs) utilize the convolutional structure to obtain an effective insight on representation by aggregating the information from neighborhoods. In order to demonstrate high performance, it is necessary to select neighborhoods that can propagate important information to target nodes, and acquire appropriate filter values during training. Recent GCNs algorithms adopt simple neighborhood selection methods, such as taking all 1-hop nodes. In the present case, unnecessary information was propagated to the target node, resulting in degradation of the performance of the model. In this paper, we propose a GCN algorithm that utilizes valid neighborhoods by calculating the similarity between the target node and neighborhoods.
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