Deep hybrid collaborative filtering for E-commerce product recommendation system

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Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu | Master's thesis
Date
2020-12-14
Department
Major/Subject
Machine Learning, Data Science and Artificial Intelligence
Mcode
SCI3042
Degree programme
Master’s Programme in Computer, Communication and Information Sciences
Language
en
Pages
42
Series
Abstract
Deep 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.
Description
Supervisor
Babbar, Rohit
Thesis advisor
Mantere, Markku
Keywords
collaborative filtering, variational autoencoders, deep learning, recommendation systems
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