no code implementations • 25 Jul 2019 • Murium Iqbal, Kamelia Aryafar, Timothy Anderton
We propose Style Conditioned Recommendations (SCR) and introduce style injection as a method to diversify recommendations.
no code implementations • 24 Jul 2019 • Murium Iqbal, Nishan Subedi, Kamelia Aryafar
The problem of ranking is a multi-billion dollar problem.
no code implementations • 28 Jun 2018 • Murium Iqbal, Adair Kovac, Kamelia Aryafar
The second system extends the first by incorporating text data and applying polylingual topic modeling to infer style over both modalities.
no code implementations • 23 Apr 2018 • Murium Iqbal, Adair Kovac, Kamelia Aryafar
In this paper, we explore Latent Dirichlet Allocation (LDA) and Polylingual Latent Dirichlet Allocation (PolyLDA), as a means to discover trending styles in Overstock from deep visual semantic features transferred from a pretrained convolutional neural network and text-based item attributes.
2 code implementations • 4 Nov 2017 • Kamelia Aryafar, Devin Guillory, Liangjie Hong
In this paper, we provide a holistic view of Etsy's promoted listings' CTR prediction system and propose an ensemble learning approach which is based on historical or behavioral signals for older listings as well as content-based features for new listings.
no code implementations • 12 Dec 2016 • Arjun Raj Rajanna, Kamelia Aryafar, Rajeev Ramchandran, Christye Sisson, Ali Shokoufandeh, Raymond Ptucha
Our experimental results show that neural networks in combination with preprocessing on the images can boost the classification accuracy on this dataset.
no code implementations • 12 May 2016 • Stephen Zakrewsky, Kamelia Aryafar, Ali Shokoufandeh
In this paper we use a set of image features that indicate quality to predict product listing popularity on a major e-commerce website, Etsy.
no code implementations • 10 May 2016 • Yasamin Alkhorshid, Kamelia Aryafar, Sven Bauer, Gerd Wanielik
Autonomous driving is a rapidly evolving technology.
no code implementations • 20 Nov 2015 • Corey Lynch, Kamelia Aryafar, Josh Attenberg
As a result, the task of ranking search results automatically (learning to rank) is a multibillion dollar machine learning problem.