An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy

Etsy is a global marketplace where people across the world connect to make, buy and sell unique goods. Sellers at Etsy can promote their product listings via advertising campaigns similar to traditional sponsored search ads. Click-Through Rate (CTR) prediction is an integral part of online search advertising systems where it is utilized as an input to auctions which determine the final ranking of promoted listings to a particular user for each query. 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. We obtain representations from texts and images by utilizing state-of-the-art deep learning techniques and employ multimodal learning to combine these different signals. We compare the system to non-trivial baselines on a large-scale real world dataset from Etsy, demonstrating the effectiveness of the model and strong correlations between offline experiments and online performance. The paper is also the first technical overview to this kind of product in e-commerce context

Style Conditioned Recommendations

We propose Style Conditioned Recommendations (SCR) and introduce style injection as a method to diversify recommendations. We use Conditional Variational Autoencoder (CVAE) architecture, where both the encoder and decoder are conditioned on a user profile learned from item content data. This allows us to apply style transfer methodologies to the task of recommendations, which we refer to as injection. To enable style injection, user profiles are learned to be interpretable such that they express users' propensities for specific predefined styles. These are learned via label-propagation from a dataset of item content, with limited labeled points. To perform injection, the condition on the encoder is learned while the condition on the decoder is selected per explicit feedback. Explicit feedback can be taken either from a user's response to a style or interest quiz, or from item ratings. In the absence of explicit feedback, the condition at the encoder is applied to the decoder. We show a 12% improvement on NDCG@20 over the traditional VAE based approach and an average 22% improvement on AUC across all classes for predicting user style profiles against our best performing baseline. After injecting styles we compare the user style profile to the style of the recommendations and show that injected styles have an average +133% increase in presence. Our results show that style injection is a powerful method to diversify recommendations while maintaining personal relevance. Our main contribution is an application of a semi-supervised approach that extends item labels to interpretable user profiles.Comment: 9 pages, 10 figures, Accepted to RecSys '1

Music Genre Classification Using Explicit Semantic Analysis

Presented at the ACM Multimedia 2011 Doctoral Symposium Poster, Scottsdale, Arizona, USA.Music genre classification is the categorization of a piece of music into its corresponding categorical labels created by humans and has been traditionally performed through a manual process. Automatic music genre classification, a fundamental problem in the musical information retrieval community, has been gaining more attention with advances in the development of the digital music industry. Most current genre classification methods tend to be based on the extraction of short-time features in combination with high-level audio features to perform genre classification. However, the representation of short-time features, using time windows, in a semantic space has received little attention. This paper proposes a vector space model of mel-frequency cepstral coefficients (MFCCs) that can, in turn, be used by a supervised learning schema for music genre classification. Inspired by explicit semantic analysis of textual documents using term frequency-inverse document frequency (tf-idf), a semantic space model is proposed to represent music samples. The effectiveness of this representation of audio samples is then demonstrated in music genre classification using various machine learning classification algorithms, including support vector machines (SVMs) and k-nearest neighbor clustering. Our preliminary results suggest that the proposed method is comparable to genre classification methods that use low-level audio features

Automatic Classification of Digital Music by Genre

Presented at the Grace Hopper Celebration of Women in Computing (GHC’12) Research Poster, Baltimore, MD, USA and also presented at the Women in Machine Learning Workshop (WiML ’12), Research Poster, Lake Tahoe, Nevada, USA.Over the past two decades, advances in the digital music industry have resulted in an exponential growth in music data sets. This exponential growth has in turn spurred great interest in music information retrieval (MIR) problems, organizing large music collections, and content-based search methods for digital music libraries. Equally important are the related problems in music classification such as genre classification, music mood analysis, and artist identification. Music genre classification is a well-studied problem in the music information retrieval community and has a wide range of applications. In this project we address the problem of genre classification by representing the MFCC feature vectors in an extended semantic space. We combine this audio representation with machine learning techniques to perform genre classification with the goal of obtaining higher classification accuracy