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Information × Registration Number 2119U006544, Article popup.category Препринт Title Convolutional Graph Embeddings for article recommendation in Wikipedia (AI translated) popup.author Moskalenko OleksiiMoskalenko Oleksii popup.publication 01-01-2019 popup.source_user Український католицький університет popup.source https://hdl.handle.net/20.500.14570/1333 popup.publisher Description In this master thesis, we were solving the task of a recommendation system to recommend articles to edit to Wikipedia contributors. Our system is built on top of articles’ embeddings constructed by applying Graph Convolutional Network to the graph of Wikipedia articles. We outperformed embeddings generated from the text (via Doc2Vec model) by 47% in Recall and 32% in Mean Reciprocal Rank (MRR) score for English Wikipedia and by 62% in Recall and 41% in MRR for Ukrainian in the offline evaluation conducted on the history of previous users’ editions. With the additional ranking model we were able to achieve total improvement on 68% in Recall and 41% in MRR on English edition of Wikipedia. Graph Neural Networks are deep learning based methods aimed to solve typical Machine Learning tasks such as classification, clusterization or link prediction for structured data - Graphs - via message passing architecture. Due to the explosive success of Convolution Neural Networks (CNN) in the construction of highly expressive representations - similar ideas were recently projected onto GNN. Graph Convolutional Networks are GNNs that likewise CNNs allow sharing weights for convolutional filters across nodes in the graph. They demonstrated especially good performance on the task of Representation Learning via semi-supervised tasks as mentioned above classification or link-prediction. popup.nrat_date 2025-11-05 Close