18,538 research outputs found
Deep Hierarchical Classification for Category Prediction in E-commerce System
In e-commerce system, category prediction is to automatically predict
categories of given texts. Different from traditional classification where
there are no relations between classes, category prediction is reckoned as a
standard hierarchical classification problem since categories are usually
organized as a hierarchical tree. In this paper, we address hierarchical
category prediction. We propose a Deep Hierarchical Classification framework,
which incorporates the multi-scale hierarchical information in neural networks
and introduces a representation sharing strategy according to the category
tree. We also define a novel combined loss function to punish hierarchical
prediction losses. The evaluation shows that the proposed approach outperforms
existing approaches in accuracy.Comment: 5pages, to be published in ECNLP workshop of ACL2
TXtract: Taxonomy-Aware Knowledge Extraction for Thousands of Product Categories
Extracting structured knowledge from product profiles is crucial for various
applications in e-Commerce. State-of-the-art approaches for knowledge
extraction were each designed for a single category of product, and thus do not
apply to real-life e-Commerce scenarios, which often contain thousands of
diverse categories. This paper proposes TXtract, a taxonomy-aware knowledge
extraction model that applies to thousands of product categories organized in a
hierarchical taxonomy. Through category conditional self-attention and
multi-task learning, our approach is both scalable, as it trains a single model
for thousands of categories, and effective, as it extracts category-specific
attribute values. Experiments on products from a taxonomy with 4,000 categories
show that TXtract outperforms state-of-the-art approaches by up to 10% in F1
and 15% in coverage across all categories.Comment: Accepted to ACL 2020 (Long Paper
A Unified Model with Structured Output for Fashion Images Classification
A picture is worth a thousand words. Albeit a clich\'e, for the fashion
industry, an image of a clothing piece allows one to perceive its category
(e.g., dress), sub-category (e.g., day dress) and properties (e.g., white
colour with floral patterns). The seasonal nature of the fashion industry
creates a highly dynamic and creative domain with evermore data, making it
unpractical to manually describe a large set of images (of products). In this
paper, we explore the concept of visual recognition for fashion images through
an end-to-end architecture embedding the hierarchical nature of the annotations
directly into the model. Towards that goal, and inspired by the work of [7], we
have modified and adapted the original architecture proposal. Namely, we have
removed the message passing layer symmetry to cope with Farfetch category tree,
added extra layers for hierarchy level specificity, and moved the message
passing layer into an enriched latent space. We compare the proposed unified
architecture against state-of-the-art models and demonstrate the performance
advantage of our model for structured multi-level categorization on a dataset
of about 350k fashion product images.Comment: Accepted in KDD 2018's AI for Fashion worksho
Product Classification in E-Commerce using Distributional Semantics
Product classification is the task of automatically predicting a taxonomy
path for a product in a predefined taxonomy hierarchy given a textual product
description or title. For efficient product classification we require a
suitable representation for a document (the textual description of a product)
feature vector and efficient and fast algorithms for prediction. To address the
above challenges, we propose a new distributional semantics representation for
document vector formation. We also develop a new two-level ensemble approach
utilizing (with respect to the taxonomy tree) a path-wise, node-wise and
depth-wise classifiers for error reduction in the final product classification.
Our experiments show the effectiveness of the distributional representation and
the ensemble approach on data sets from a leading e-commerce platform and
achieve better results on various evaluation metrics compared to earlier
approaches
Don't Classify, Translate: Multi-Level E-Commerce Product Categorization Via Machine Translation
E-commerce platforms categorize their products into a multi-level taxonomy
tree with thousands of leaf categories. Conventional methods for product
categorization are typically based on machine learning classification
algorithms. These algorithms take product information as input (e.g., titles
and descriptions) to classify a product into a leaf category. In this paper, we
propose a new paradigm based on machine translation. In our approach, we
translate a product's natural language description into a sequence of tokens
representing a root-to-leaf path in a product taxonomy. In our experiments on
two large real-world datasets, we show that our approach achieves better
predictive accuracy than a state-of-the-art classification system for product
categorization. In addition, we demonstrate that our machine translation models
can propose meaningful new paths between previously unconnected nodes in a
taxonomy tree, thereby transforming the taxonomy into a directed acyclic graph
(DAG). We discuss how the resultant taxonomy DAG promotes user-friendly
navigation, and how it is more adaptable to new products
How to Grow a (Product) Tree: Personalized Category Suggestions for eCommerce Type-Ahead
In an attempt to balance precision and recall in the search page, leading
digital shops have been effectively nudging users into select category facets
as early as in the type-ahead suggestions. In this work, we present
SessionPath, a novel neural network model that improves facet suggestions on
two counts: first, the model is able to leverage session embeddings to provide
scalable personalization; second, SessionPath predicts facets by explicitly
producing a probability distribution at each node in the taxonomy path. We
benchmark SessionPath on two partnering shops against count-based and neural
models, and show how business requirements and model behavior can be combined
in a principled way
Time Perception Machine: Temporal Point Processes for the When, Where and What of Activity Prediction
Numerous powerful point process models have been developed to understand
temporal patterns in sequential data from fields such as health-care,
electronic commerce, social networks, and natural disaster forecasting. In this
paper, we develop novel models for learning the temporal distribution of human
activities in streaming data (e.g., videos and person trajectories). We propose
an integrated framework of neural networks and temporal point processes for
predicting when the next activity will happen. Because point processes are
limited to taking event frames as input, we propose a simple yet effective
mechanism to extract features at frames of interest while also preserving the
rich information in the remaining frames. We evaluate our model on two
challenging datasets. The results show that our model outperforms traditional
statistical point process approaches significantly, demonstrating its
effectiveness in capturing the underlying temporal dynamics as well as the
correlation within sequential activities. Furthermore, we also extend our model
to a joint estimation framework for predicting the timing, spatial location,
and category of the activity simultaneously, to answer the when, where, and
what of activity prediction
Anomaly Detection for an E-commerce Pricing System
Online retailers execute a very large number of price updates when compared
to brick-and-mortar stores. Even a few mis-priced items can have a significant
business impact and result in a loss of customer trust. Early detection of
anomalies in an automated real-time fashion is an important part of such a
pricing system. In this paper, we describe unsupervised and supervised anomaly
detection approaches we developed and deployed for a large-scale online pricing
system at Walmart. Our system detects anomalies both in batch and real-time
streaming settings, and the items flagged are reviewed and actioned based on
priority and business impact. We found that having the right architecture
design was critical to facilitate model performance at scale, and business
impact and speed were important factors influencing model selection, parameter
choice, and prioritization in a production environment for a large-scale
system. We conducted analyses on the performance of various approaches on a
test set using real-world retail data and fully deployed our approach into
production. We found that our approach was able to detect the most important
anomalies with high precision.Comment: 10 pages, 4 figure
Deep Cascade Multi-task Learning for Slot Filling in Online Shopping Assistant
Slot filling is a critical task in natural language understanding (NLU) for
dialog systems. State-of-the-art approaches treat it as a sequence labeling
problem and adopt such models as BiLSTM-CRF. While these models work relatively
well on standard benchmark datasets, they face challenges in the context of
E-commerce where the slot labels are more informative and carry richer
expressions. In this work, inspired by the unique structure of E-commerce
knowledge base, we propose a novel multi-task model with cascade and residual
connections, which jointly learns segment tagging, named entity tagging and
slot filling. Experiments show the effectiveness of the proposed cascade and
residual structures. Our model has a 14.6% advantage in F1 score over the
strong baseline methods on a new Chinese E-commerce shopping assistant dataset,
while achieving competitive accuracies on a standard dataset. Furthermore,
online test deployed on such dominant E-commerce platform shows 130%
improvement on accuracy of understanding user utterances. Our model has already
gone into production in the E-commerce platform.Comment: AAAI 201
Image Matters: Scalable Detection of Offensive and Non-Compliant Content / Logo in Product Images
In e-commerce, product content, especially product images have a significant
influence on a customer's journey from product discovery to evaluation and
finally, purchase decision. Since many e-commerce retailers sell items from
other third-party marketplace sellers besides their own, the content published
by both internal and external content creators needs to be monitored and
enriched, wherever possible. Despite guidelines and warnings, product listings
that contain offensive and non-compliant images continue to enter catalogs.
Offensive and non-compliant content can include a wide range of objects, logos,
and banners conveying violent, sexually explicit, racist, or promotional
messages. Such images can severely damage the customer experience, lead to
legal issues, and erode the company brand. In this paper, we present a computer
vision driven offensive and non-compliant image detection system for extremely
large image datasets. This paper delves into the unique challenges of applying
deep learning to real-world product image data from retail world. We
demonstrate how we resolve a number of technical challenges such as lack of
training data, severe class imbalance, fine-grained class definitions etc.
using a number of practical yet unique technical strategies. Our system
combines state-of-the-art image classification and object detection techniques
with budgeted crowdsourcing to develop a solution customized for a massive,
diverse, and constantly evolving product catalog.Comment: 10 page
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