Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/149716
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dc.contributor.authorChua, Song Annen_US
dc.date.accessioned2021-06-07T02:41:43Z-
dc.date.available2021-06-07T02:41:43Z-
dc.date.issued2021-
dc.identifier.citationChua, S. A. (2021). Recommendation systems based on extreme multi-label classification. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149716en_US
dc.identifier.urihttps://hdl.handle.net/10356/149716-
dc.description.abstractThis project aims to implement a recommender system using extreme multi-label classification algorithms. In the era of big data, traditional recommender systems are unable to keep up with the scale and size of data available. Extreme multi-label classification can tag a given target with multiple labels that are most relevant to it from an extremely large dataset of labels. This report summarises the design implementation and empirical studies of extreme multi-label classification algorithms for recommendation systems on the MovieLens 1M benchmark dataset. This project studied 2 tree-based extreme multi-label classification algorithms, FastXML and AttentionXML, and implemented them using Python for a movie recommender system. This was to investigate the reformulation of the recommender problem as a multi-label classification task. The dataset was prepared such that each item that can be recommended by the system was treated as a unique label that can be tagged to a user by the classifier. The 2 algorithms were compared based on accuracy as well as computational resources required. The accuracy of AttentionXML was 46.6%, 5% larger than that of FastXML’s accuracy of 41.4%. However, FastXML had a smaller computational requirement than AttentionXML. The memory footprints of AttentionXML’s models were smaller than FastXML’s models. This is because AttentionXML used more computational resources to train a deep model for each layer of its tree, while FastXML used more memory to train a larger tree ensemble to make up for the lower accuracy per tree.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.relationA3044-201en_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleRecommendation systems based on extreme multi-label classificationen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorLihui CHENen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
dc.contributor.supervisoremailELHCHEN@ntu.edu.sgen_US
item.grantfulltextrestricted-
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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