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

Machine learning approach for personalized recommendations on online platforms: uniplaces case study

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe goal of this project is to develop a model to personalize the user recommendations of an online marketplace named Uniplaces. This online business offers properties for medium and long-term stays, where landlords can directly rent their place to customers (mainly students). Whenever a student makes a reservation, the booking must be approved by the property owner. The current acceptance rate is 25%. The model is a response to this low acceptance rate, and it will have to show to each student the properties that are more likely to be accepted by the landlord. As a secondary objective, the model seeks to identify the reasons behind the landlord’s decision to accept or reject bookings. The model will be constructed using information from the users, landlord and the property itself kindly provided by Uniplaces. This information will pre-process with data cleaning, transformation and features reduction (where two techniques were applied: dimensionality reduction, features selection). After the data processing, several models were applied to the normalized data. The predictive models that will be applied are already being used on other online markets and platforms like Airbnb, Netflix or LinkedIn, namely Support Vector Machine, Neural Networks, Decision Tree, Logistic Regression and Gradient Boosting. The probability of acceptance proved to be very easy to predict, all the models predict 100% of the test dataset when using the Principal Component Analysis as the Dimensionality Reduction technique. This can be explained mainly by the fact that the new calculated features have a strong correlation with the target variable. All the algorithms predict 100% of the target variable when using Principal Component Analysis as a technique of dimensionality reduction

A Zero Attention Model for Personalized Product Search

Product search is one of the most popular methods for people to discover and purchase products on e-commerce websites. Because personal preferences often have an important influence on the purchase decision of each customer, it is intuitive that personalization should be beneficial for product search engines. While synthetic experiments from previous studies show that purchase histories are useful for identifying the individual intent of each product search session, the effect of personalization on product search in practice, however, remains mostly unknown. In this paper, we formulate the problem of personalized product search and conduct large-scale experiments with search logs sampled from a commercial e-commerce search engine. Results from our preliminary analysis show that the potential of personalization depends on query characteristics, interactions between queries, and user purchase histories. Based on these observations, we propose a Zero Attention Model for product search that automatically determines when and how to personalize a user-query pair via a novel attention mechanism. Empirical results on commercial product search logs show that the proposed model not only significantly outperforms state-of-the-art personalized product retrieval models, but also provides important information on the potential of personalization in each product search session

Neural Generative Models and Representation Learning for Information Retrieval

Information Retrieval (IR) concerns about the structure, analysis, organization, storage, and retrieval of information. Among different retrieval models proposed in the past decades, generative retrieval models, especially those under the statistical probabilistic framework, are one of the most popular techniques that have been widely applied to Information Retrieval problems. While they are famous for their well-grounded theory and good empirical performance in text retrieval, their applications in IR are often limited by their complexity and low extendability in the modeling of high-dimensional information. Recently, advances in deep learning techniques provide new opportunities for representation learning and generative models for information retrieval. In contrast to statistical models, neural models have much more flexibility because they model information and data correlation in latent spaces without explicitly relying on any prior knowledge. Previous studies on pattern recognition and natural language processing have shown that semantically meaningful representations of text, images, and many types of information can be acquired with neural models through supervised or unsupervised training. Nonetheless, the effectiveness of neural models for information retrieval is mostly unexplored. In this thesis, we study how to develop new generative models and representation learning frameworks with neural models for information retrieval. Specifically, our contributions include three main components: (1) Theoretical Analysis: We present the first theoretical analysis and adaptation of existing neural embedding models for ad-hoc retrieval tasks; (2) Design Practice: Based on our experience and knowledge, we show how to design an embedding-based neural generative model for practical information retrieval tasks such as personalized product search; And (3) Generic Framework: We further generalize our proposed neural generative framework for complicated heterogeneous information retrieval scenarios that concern text, images, knowledge entities, and their relationships. Empirical results show that the proposed neural generative framework can effectively learn information representations and construct retrieval models that outperform the state-of-the-art systems in a variety of IR tasks

Controlling the Fairness / Accuracy Tradeoff in Recommender Systems

Recommender systems are one of the most pervasive applications of machine learning. They play a pivotal role in helping users find items tailored to their taste. Although these systems intend to assist people in their information needs, they can cause implicit or explicit discrimination against individuals or groups. There are several ways that different biases can creep into recommender systems. Reflection of societal and historical prejudices in datasets and during the data collection process, lack of sufficient data on minority groups, lack of suitable evaluation methods and model designs to detect these biases and lessen the unfairness caused by them are among the many reasons for unfairness in these systems. A system needs to defend against the biases in recommendation output to prevent harm and unfairness. However, integrating the goal of fairness with accuracy in recommender systems is challenging, primarily because of this goal's significant trade-offs with accuracy. Accuracy in recommender systems is the ability of that system to predict users' needs and interests accurately. On the other hand, fairness is a complicated concept with a variety of definitions. To use fairness as an objective, we need to define it based on the application area and the context of a problem. Additionally, we need to specify the fairness concerns of the different stakeholders involved in the recommender systems and the fairness priorities of a system. Any of these aspects might disagree with the goal of accuracy. For example, if fairness for content providers is more exposure to users, increasing it might cause a reduction in accuracy. Therefore, controlling the trade-off between accuracy and fairness becomes essential. Throughout this dissertation, several recommendation models and re-ranking approaches are presented that aim to address this problem using in- and post- processing methods. These approaches show promising results, but it is worth mentioning that they have intrinsic limitations and, therefore, shouldn't be considered ultimate solutions

Museum of Contemporary Commodities: a research performance

The materialities and injustices of the 'prolific present' are overwhelming, making attention to the production, consumption and disposal of 'stuff' an urgent matter of concern. Presenting as automatic and only partially visible, creatively constructive acts of ‘dataveillance’ are integral to this explosion of stuff; conditioning our daily lives as milieus of consumption that channel profit to the propertied classes, often with socially and environmentally damaging consequences (Gabrys 2016, van Dijck, 2014, Tsing, 2013). Constructing the agency to intervene in these socio-technical valuing practices and cultural performances, requires us to consider our roles in those performances, as much as theorising the constituting structures, strategies, and (in)justices of their production. The Museum of Contemporary Commodities is an art geography research performance that is both a collaboratively produced dramaturgy of valuing, and an experiment in public curation as transformative process (Heathfield 2016, Graeber, 2013, Richter 2017). The project manifests as a series of digitally networked hacks, prototypes and events that attempt to configure new alignments between the social, material and digital that are localised and mobile, stable and reconfigurable, familiar and new (Suchman et al., 2002). These are art geographies as collectively produced critical making and social practices, which encourage audience-as-participant move from 'automatic' taking part in the unfolding immanence of the world, to feeling it more deeply. By extension attending to and caring for the ethical and political implications, and the material things that participation produces (Cull, 2011, Puig de la Bellacasa 2012)

2022, UMaine News Press Releases

This is a catalog of press releases put out by the University of Maine Division of Marketing and Communications between January 3, 2022 and October 17, 2022

Design revolutions: IASDR 2019 Conference Proceedings. Volume 1: Change, Voices, Open

In September 2019 Manchester School of Art at Manchester Metropolitan University was honoured to host the bi-annual conference of the International Association of Societies of Design Research (IASDR) under the unifying theme of DESIGN REVOLUTIONS. This was the first time the conference had been held in the UK. Through key research themes across nine conference tracks – Change, Learning, Living, Making, People, Technology, Thinking, Value and Voices – the conference opened up compelling, meaningful and radical dialogue of the role of design in addressing societal and organisational challenges. This Volume 1 includes papers from Change, Voices and Open tracks of the conference