Improving Deep Learning Based Recommender Systems Using Dimensionality Reduction Methodologies

Jawad, Mohammad Anas; Islam, Mohammed Saidul

dc.contributor.author	Jawad, Mohammad Anas
dc.contributor.author	Islam, Mohammed Saidul
dc.date.accessioned	2020-10-28T09:41:10Z
dc.date.available	2020-10-28T09:41:10Z
dc.date.issued	2019-11-15
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dc.identifier.uri	http://hdl.handle.net/123456789/615
dc.description	Supervised by Prof. Dr. Abu Raihan Mostofa Kamal	en_US
dc.description.abstract	Understanding sophisticated user behavior-based feature interaction is crucial to optimizing CTR for recommender systems. Despite having significant progress, the methods that are used nowadays tend to have a strong bias towards low- or higher-order feature interactions and involve a great deal of feature engineering. However, most of the feature engineering methods are nontrivial and often requires rigorous feature engineering or exhaustive searching. DNNs can learn feature interactions automatically; but they implicitly generate all of those interactions and there is no control over the DNN about how it is generating all the cross features thus resulting in many redundant crosses. The proposed model, incorporates the strength of factorization machines for recommendation and applies PCA for learning largest data variance to feed the important features to the deep model. Compared to the Google’s newWide Deep design, we used the regular input for the factorizationmachines, but to get rid of the redundant cross features we feed the deep model with major features that impacts the prediction most. The results are very promising and they show a significant increase in accuracy on the predictions for CTR compared to state of the artWide & Deep and DeepFM model.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering, Islamic University of Technology, Gazipur, Bangladesh	en_US
dc.title	Improving Deep Learning Based Recommender Systems Using Dimensionality Reduction Methodologies	en_US
dc.type	Thesis	en_US