936 research outputs found
A Deep Learning System for Predicting Size and Fit in Fashion E-Commerce
Personalized size and fit recommendations bear crucial significance for any
fashion e-commerce platform. Predicting the correct fit drives customer
satisfaction and benefits the business by reducing costs incurred due to
size-related returns. Traditional collaborative filtering algorithms seek to
model customer preferences based on their previous orders. A typical challenge
for such methods stems from extreme sparsity of customer-article orders. To
alleviate this problem, we propose a deep learning based content-collaborative
methodology for personalized size and fit recommendation. Our proposed method
can ingest arbitrary customer and article data and can model multiple
individuals or intents behind a single account. The method optimizes a global
set of parameters to learn population-level abstractions of size and fit
relevant information from observed customer-article interactions. It further
employs customer and article specific embedding variables to learn their
properties. Together with learned entity embeddings, the method maps additional
customer and article attributes into a latent space to derive personalized
recommendations. Application of our method to two publicly available datasets
demonstrate an improvement over the state-of-the-art published results. On two
proprietary datasets, one containing fit feedback from fashion experts and the
other involving customer purchases, we further outperform comparable
methodologies, including a recent Bayesian approach for size recommendation.Comment: Published at the Thirteenth ACM Conference on Recommender Systems
(RecSys '19), September 16--20, 2019, Copenhagen, Denmar
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Modeling the Dynamics of Consumer Behavior from Massive Interaction Data
Recent technological innovations (e.g. e-commerce platforms, automated retail stores) have enabled dramatic changes in people's shopping experiences, as well as the accessibility to incredible volumes of consumer-product interaction data. As a result, machine learning (ML) systems can be widely developed to help people navigate relevant information and make decisions. Traditional ML systems have achieved great success on various well-defined problems such as speech recognition and facial recognition. Unlike these tasks where datasets and objectives are clearly benchmarked, modeling consumer behavior can be rather complicated; for example, consumer activities can be affected by real-time shopping contexts, collected interaction data can be noisy and biased, interests from multiple parties (both consumers and producers) can be involved in the predictive objectives.The primary goal of this dissertation is to address the obstacles in modeling consumer activities through computational approaches, but with careful considerations from economic and societal perspectives. Intellectually, such models help us to understand the forces that guide consumer behavior. Methodologically, I build algorithms capable of processing massive interaction datasets by connecting well-developed ML techniques and well-established economic theories. Practically, my work has applications ranging from recommender systems, e-commerce and business intelligence
Computational Technologies for Fashion Recommendation: A Survey
Fashion recommendation is a key research field in computational fashion
research and has attracted considerable interest in the computer vision,
multimedia, and information retrieval communities in recent years. Due to the
great demand for applications, various fashion recommendation tasks, such as
personalized fashion product recommendation, complementary (mix-and-match)
recommendation, and outfit recommendation, have been posed and explored in the
literature. The continuing research attention and advances impel us to look
back and in-depth into the field for a better understanding. In this paper, we
comprehensively review recent research efforts on fashion recommendation from a
technological perspective. We first introduce fashion recommendation at a macro
level and analyse its characteristics and differences with general
recommendation tasks. We then clearly categorize different fashion
recommendation efforts into several sub-tasks and focus on each sub-task in
terms of its problem formulation, research focus, state-of-the-art methods, and
limitations. We also summarize the datasets proposed in the literature for use
in fashion recommendation studies to give readers a brief illustration.
Finally, we discuss several promising directions for future research in this
field. Overall, this survey systematically reviews the development of fashion
recommendation research. It also discusses the current limitations and gaps
between academic research and the real needs of the fashion industry. In the
process, we offer a deep insight into how the fashion industry could benefit
from fashion recommendation technologies. the computational technologies of
fashion recommendation
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