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dc.contributor.advisorHuang, Xiangji
dc.contributor.authorHu, Haohao
dc.description.abstractIn modern e-commerce systems, large volumes of new items are being added to the product list everyday, which calls for automatic product categorization. In this thesis we propose a weighted K-Nearest Neighbour (KNN) based classification system for solving large-scale e-commerce product taxonomy classification problem. We use information retrieval (IR) model as similarity function in our weighted KNN algorithm. Among all IR models used in this study, we achieved highest classification performance through using information-based (IB) model as similarity function in the KNN algorithm. Moreover, our proposed method can improve the overall performance when combining prediction results with those from advanced neural network based method, namely Long Short-Term Memory with Balanced Pooling Views (LSTM-BPV). The hybrid system could achieve results comparable to the state of the art (SotA). We also get good results by fine-tuning pre-trained Bidirectional Encoder Representations from Transformers (BERT) model.
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectArtificial intelligence
dc.titleA Hybrid Approach for Large-Scale Product Categorization Based on Weighted KNN and LSTM-BPV
dc.typeElectronic Thesis or Dissertation Systems and Technology - Master of Arts's
dc.subject.keywordsE-commerce Product Taxonomy Classification
dc.subject.keywordsInformation Retrieval
dc.subject.keywordsK-Nearest Neighbour
dc.subject.keywordsText Classification
dc.subject.keywordsData Mining

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