29 research outputs found
Knowledge-aware Complementary Product Representation Learning
Learning product representations that reflect complementary relationship
plays a central role in e-commerce recommender system. In the absence of the
product relationships graph, which existing methods rely on, there is a need to
detect the complementary relationships directly from noisy and sparse customer
purchase activities. Furthermore, unlike simple relationships such as
similarity, complementariness is asymmetric and non-transitive. Standard usage
of representation learning emphasizes on only one set of embedding, which is
problematic for modelling such properties of complementariness. We propose
using knowledge-aware learning with dual product embedding to solve the above
challenges. We encode contextual knowledge into product representation by
multi-task learning, to alleviate the sparsity issue. By explicitly modelling
with user bias terms, we separate the noise of customer-specific preferences
from the complementariness. Furthermore, we adopt the dual embedding framework
to capture the intrinsic properties of complementariness and provide geometric
interpretation motivated by the classic separating hyperplane theory. Finally,
we propose a Bayesian network structure that unifies all the components, which
also concludes several popular models as special cases. The proposed method
compares favourably to state-of-art methods, in downstream classification and
recommendation tasks. We also develop an implementation that scales efficiently
to a dataset with millions of items and customers
GNN-GMVO: Graph Neural Networks for Optimizing Gross Merchandise Value in Similar Item Recommendation
Similar item recommendation is a critical task in the e-Commerce industry,
which helps customers explore similar and relevant alternatives based on their
interested products. Despite the traditional machine learning models, Graph
Neural Networks (GNNs), by design, can understand complex relations like
similarity between products. However, in contrast to their wide usage in
retrieval tasks and their focus on optimizing the relevance, the current GNN
architectures are not tailored toward maximizing revenue-related objectives
such as Gross Merchandise Value (GMV), which is one of the major business
metrics for e-Commerce companies. In addition, defining accurate edge relations
in GNNs is non-trivial in large-scale e-Commerce systems, due to the
heterogeneity nature of the item-item relationships. This work aims to address
these issues by designing a new GNN architecture called GNN-GMVO (Graph Neural
Network - Gross Merchandise Value Optimizer). This model directly optimizes GMV
while considering the complex relations between items. In addition, we propose
a customized edge construction method to tailor the model toward similar item
recommendation task and alleviate the noisy and complex item-item relations. In
our comprehensive experiments on three real-world datasets, we show higher
prediction performance and expected GMV for top ranked items recommended by our
model when compared with selected state-of-the-art benchmark models.Comment: 9 pages, 3 figures, 43 citation
Enhanced E-Commerce Attribute Extraction: Innovating with Decorative Relation Correction and LLAMA 2.0-Based Annotation
The rapid proliferation of e-commerce platforms accentuates the need for
advanced search and retrieval systems to foster a superior user experience.
Central to this endeavor is the precise extraction of product attributes from
customer queries, enabling refined search, comparison, and other crucial
e-commerce functionalities. Unlike traditional Named Entity Recognition (NER)
tasks, e-commerce queries present a unique challenge owing to the intrinsic
decorative relationship between product types and attributes. In this study, we
propose a pioneering framework that integrates BERT for classification, a
Conditional Random Fields (CRFs) layer for attribute value extraction, and
Large Language Models (LLMs) for data annotation, significantly advancing
attribute recognition from customer inquiries. Our approach capitalizes on the
robust representation learning of BERT, synergized with the sequence decoding
prowess of CRFs, to adeptly identify and extract attribute values. We introduce
a novel decorative relation correction mechanism to further refine the
extraction process based on the nuanced relationships between product types and
attributes inherent in e-commerce data. Employing LLMs, we annotate additional
data to expand the model's grasp and coverage of diverse attributes. Our
methodology is rigorously validated on various datasets, including Walmart,
BestBuy's e-commerce NER dataset, and the CoNLL dataset, demonstrating
substantial improvements in attribute recognition performance. Particularly,
the model showcased promising results during a two-month deployment in
Walmart's Sponsor Product Search, underscoring its practical utility and
effectiveness.Comment: 9 pages, 5 image
Leveraging Large Language Models for Enhanced Product Descriptions in eCommerce
In the dynamic field of eCommerce, the quality and comprehensiveness of
product descriptions are pivotal for enhancing search visibility and customer
engagement. Effective product descriptions can address the 'cold start'
problem, align with market trends, and ultimately lead to increased
click-through rates. Traditional methods for crafting these descriptions often
involve significant human effort and may lack both consistency and scalability.
This paper introduces a novel methodology for automating product description
generation using the LLAMA 2.0 7B language model. We train the model on a
dataset of authentic product descriptions from Walmart, one of the largest
eCommerce platforms. The model is then fine-tuned for domain-specific language
features and eCommerce nuances to enhance its utility in sales and user
engagement. We employ multiple evaluation metrics, including NDCG, customer
click-through rates, and human assessments, to validate the effectiveness of
our approach. Our findings reveal that the system is not only scalable but also
significantly reduces the human workload involved in creating product
descriptions. This study underscores the considerable potential of large
language models like LLAMA 2.0 7B in automating and optimizing various facets
of eCommerce platforms, offering significant business impact, including
improved search functionality and increased sales.Comment: 9 pages, 4 figures, EMNLP2023 workshop, The 2023 Conference on
Empirical Methods in Natural Language Processin
Multimodal sequential fashion attribute prediction
We address multimodal product attribute prediction of fashion items based on product images and titles. The product attributes, such as type, sub-type, cut or fit, are in a chain format, with previous attribute values constraining the values of the next attributes. We propose to address this task with a sequential prediction model that can learn to capture the dependencies between the different attribute values in the chain. Our experiments on three product datasets show that the sequential model outperforms two non-sequential baselines on all experimental datasets. Compared to other models, the sequential model is also better able to generate sequences of attribute chains not seen during training. We also measure the contributions of both image and textual input and show that while text-only models always outperform image-only models, only the multimodal sequential model combining both image and text improves over the text-only model on all experimental dataset
Classification of retail products: From probabilistic ranking to neural networks
Food retailing is now on an accelerated path to a success penetration into
the digital market by new ways of value creation at all stages of the consumer
decision process. One of the most important imperatives in this path is the
availability of quality data to feed all the process in digital transformation.
But the quality of data is not so obvious if we consider the variety of
products and suppliers in the grocery market. Within this context of digital
transformation of grocery industry, \textit{Midiadia} is Spanish data provider
company that works on converting data from the retailers' products into
knowledge with attributes and insights from the product labels, that is,
maintaining quality data in a dynamic market with a high dispersion of
products. Currently, they manually categorize products (groceries) according to
the information extracted directly (text processing) from the product labelling
and packaging. This paper introduces a solution to automatically categorize the
constantly changing product catalogue into a 3-level food taxonomy. Our
proposal studies three different approaches: a score-based ranking method,
traditional machine learning algorithms, and deep neural networks. Thus, we
provide four different classifiers that support a more efficient and less
error-prone maintenance of groceries catalogues, the main asset of the company.
Finally, we have compared the performance of these three alternatives,
concluding that traditional machine learning algorithms perform better, but
closely followed by the score-based approach.Comment: 17 pages, 8 figures, journa