Deep hybrid collaborative filtering for E-commerce product recommendation system

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.advisorMantere, Markku
dc.contributor.authorMemon, Muhammad
dc.contributor.schoolPerustieteiden korkeakoulufi
dc.contributor.supervisorBabbar, Rohit
dc.date.accessioned2020-12-20T18:10:40Z
dc.date.available2020-12-20T18:10:40Z
dc.date.issued2020-12-14
dc.description.abstractDeep learning based approaches for collaborative filtering have proven highly successful in modern recommendation system research. In this work, we explore Variational Autoencoders to build a product recommendation system. We utilize modern frameworks of probabilistic deep learning to carry out an empirical analysis on real world datasets and compare it with traditional matrix factorization methods. Matrix factorization methods for collaborative filtering suffered from sparsity in the data and under performed on scalability measures. We use these methods as baselines and strive to outperform them. We augment a standard Variational Autoencoder with a regularization parameter that partially anneals the divergence term in the objective function and use multinomial likelihood to model the user-item interaction data. To analyse our methods, we use two recently collected datasets comprising of user and item interactions. We run various experiments on these datasets to find the best possible model. We report a quantitative comparison between the baselines and our approach on various metrics. We reach satisfactory results suggesting that the partially regularized Variational Autoencoder framework with a multinomial likelihood is well suited for the collaborative filtering task on real world data.en
dc.format.extent42
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/97570
dc.identifier.urnURN:NBN:fi:aalto-2020122056397
dc.language.isoenen
dc.programmeMaster’s Programme in Computer, Communication and Information Sciencesfi
dc.programme.majorMachine Learning, Data Science and Artificial Intelligencefi
dc.programme.mcodeSCI3042fi
dc.subject.keywordcollaborative filteringen
dc.subject.keywordvariational autoencodersen
dc.subject.keyworddeep learningen
dc.subject.keywordrecommendation systemsen
dc.titleDeep hybrid collaborative filtering for E-commerce product recommendation systemen
dc.typeG2 Pro gradu, diplomityöfi
dc.type.ontasotMaster's thesisen
dc.type.ontasotDiplomityöfi
local.aalto.electroniconlyyes
local.aalto.openaccessyes
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