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Found 9 papers, 1 papers with code
Style Conditioned Recommendations
We propose Style Conditioned Recommendations (SCR) and introduce style injection as a method to diversify recommendations.
A Multimodal Recommender System for Large-scale Assortment Generation in E-commerce
The second system extends the first by incorporating text data and applying polylingual topic modeling to infer style over both modalities.
Discovering Style Trends through Deep Visually Aware Latent Item Embeddings
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.
An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy
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.
Neural Networks with Manifold Learning for Diabetic Retinopathy Detection
Our experimental results show that neural networks in combination with preprocessing on the images can boost the classification accuracy on this dataset.
Item Popularity Prediction in E-commerce Using Image Quality Feature Vectors
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.
Road Detection through Supervised Classification
Autonomous driving is a rapidly evolving technology.
Images Don't Lie: Transferring Deep Visual Semantic Features to Large-Scale Multimodal Learning to Rank
As a result, the task of ranking search results automatically (learning to rank) is a multibillion dollar machine learning problem.
