Convolutional Graph Embeddings for article recommendation in Wikipedia

Moskalenko, Oleksii

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dc.contributor.author	Moskalenko, Oleksii
dc.date.accessioned	2019-02-19T15:11:39Z
dc.date.available	2019-02-19T15:11:39Z
dc.date.issued	2019
dc.identifier.citation	Moskalenko, Oleksii. Convolutional Graph Embeddings for article recommendation in Wikipedia : Master Thesis : manuscript / Oleksii Moskalenko ; Supervisor Diego Sáez-Trumper ; Ukrainian Catholic University, Department of Computer Sciences. – Lviv : [s.n.], 2019. – 35 p. : ill.	uk
dc.identifier.uri	http://er.ucu.edu.ua/handle/1/1333
dc.language.iso	en	uk
dc.subject	Convolutional Graph	uk
dc.subject	Wikipedia dataset	uk
dc.title	Convolutional Graph Embeddings for article recommendation in Wikipedia	uk
dc.type	Preprint	uk
dc.status	Публікується вперше	uk
dc.description.abstracten	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.	uk