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Found 38 papers, 8 papers with code
Prompt Optimizer of Text-to-Image Diffusion Models for Abstract Concept Understanding
The dataset is created with GPT-4 to extend the abstract concept to a scene and concrete objects.
LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extraction
Product attribute value extraction is a pivotal component in Natural Language Processing (NLP) and the contemporary e-commerce industry.
Chaining text-to-image and large language model: A novel approach for generating personalized e-commerce banners
Recent literature has surveyed the use of text-to-image models for enhancing the work of many creative artists.
Event-based Product Carousel Recommendation with Query-Click Graph
Product recommendations for the multiple aspects of the target event are usually generated by human curators who manually identify the aspects and select a list of aspect-related products (i. e., product carousel) for each aspect as recommendations.
Character-based Outfit Generation with Vision-augmented Style Extraction via LLMs
The outfit generation problem involves recommending a complete outfit to a user based on their interests.
LLMs with User-defined Prompts as Generic Data Operators for Reliable Data Processing
Although the UDF design pattern introduces flexibility, reusability and scalability, the increasing demand on machine learning pipelines brings three new challenges to this design pattern -- not low-code, not dependency-free and not knowledge-aware.
Seller-side Outcome Fairness in Online Marketplaces
This paper aims to investigate and achieve seller-side fairness within online marketplaces, where many sellers and their items are not sufficiently exposed to customers in an e-commerce platform.
LLM-TAKE: Theme Aware Keyword Extraction Using Large Language Models
We call our LLM-based framework Theme-Aware Keyword Extraction (LLM TAKE).
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.
Knowledge Graph Completion Models are Few-shot Learners: An Empirical Study of Relation Labeling in E-commerce with LLMs
Knowledge Graphs (KGs) play a crucial role in enhancing e-commerce system performance by providing structured information about entities and their relationships, such as complementary or substitutable relations between products or product types, which can be utilized in recommender systems.
Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders
Considering the influence of historical purchases on users' future interests, the user and item representations can be viewed as unobserved confounders in the causal diagram.
Causal Structure Learning with Recommendation System
Discovering the causal mechanism from RS feedback data is both novel and challenging, since RS itself is a source of intervention that can influence both the users' exposure and their willingness to interact.
NEAT: A Label Noise-resistant Complementary Item Recommender System with Trustworthy Evaluation
On the other hand, the model evaluation will not be trustworthy if the labels for evaluation are not reflecting the true complementary relatedness.
Generating Rich Product Descriptions for Conversational E-commerce Systems
We propose eBERT, a sequence-to-sequence approach by further pre-training the BERT embeddings on an e-commerce product description corpus, and then fine-tuning the resulting model to generate short, natural, spoken language titles from input web titles.
Pre-training Recommender Systems via Reinforced Attentive Multi-relational Graph Neural Network
In this paper, we propose a novel Reinforced Attentive Multi-relational Graph Neural Network (RAM-GNN) to the pre-train user and item embeddings on the user and item graph prior to the recommendation step.
Towards the D-Optimal Online Experiment Design for Recommender Selection
Selecting the optimal recommender via online exploration-exploitation is catching increasing attention where the traditional A/B testing can be slow and costly, and offline evaluations are prone to the bias of history data.
Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives
The recent work by Rendle et al. (2020), based on empirical observations, argues that matrix-factorization collaborative filtering (MCF) compares favorably to neural collaborative filtering (NCF), and conjectures the dot product's superiority over the feed-forward neural network as similarity function.
Variational Inference for Category Recommendation in E-Commerce platforms
Category recommendation for users on an e-Commerce platform is an important task as it dictates the flow of traffic through the website.
A Temporal Kernel Approach for Deep Learning with Continuous-time Information
Sequential deep learning models such as RNN, causal CNN and attention mechanism do not readily consume continuous-time information.
Theoretical Understandings of Product Embedding for E-commerce Machine Learning
The generalization performance in the downstream machine learning task is controlled by the alignment between the embeddings and the product relatedness measure.
GAN-based Recommendation with Positive-Unlabeled Sampling
Recommender systems are popular tools for information retrieval tasks on a large variety of web applications and personalized products.
A Real-Time Whole Page Personalization Framework for E-Commerce
In production, our system resulted in an improvement in item discovery, an increase in online engagement, and a significant lift on add-to-carts (ATCs) per visitor on the homepage.
On Detecting Data Pollution Attacks On Recommender Systems Using Sequential GANs
In this work, we propose a semi-supervised attack detection algorithm to identify the malicious datapoints.
On Variational Inference for User Modeling in Attribute-Driven Collaborative Filtering
Recommender Systems have become an integral part of online e-Commerce platforms, driving customer engagement and revenue.
Adversarial Counterfactual Learning and Evaluation for Recommender System
The feedback data of recommender systems are often subject to what was exposed to the users; however, most learning and evaluation methods do not account for the underlying exposure mechanism.
An End-to-End ML System for Personalized Conversational Voice Models in Walmart E-Commerce
Searching for and making decisions about products is becoming increasingly easier in the e-commerce space, thanks to the evolution of recommender systems.
Basket Recommendation with Multi-Intent Translation Graph Neural Network
The problem of basket recommendation~(BR) is to recommend a ranking list of items to the current basket.
Product Title Generation for Conversational Systems using BERT
Through recent advancements in speech technology and introduction of smart devices, such as Amazon Alexa and Google Home, increasing number of users are interacting with applications through voice.
Inductive Representation Learning on Temporal Graphs
Moreover, node and topological features can be temporal as well, whose patterns the node embeddings should also capture.
BasConv: Aggregating Heterogeneous Interactions for Basket Recommendation with Graph Convolutional Neural Network
By defining a basket entity to represent the basket intent, we can model this problem as a basket-item link prediction task in the User-Basket-Item~(UBI) graph.
Product Knowledge Graph Embedding for E-commerce
In this paper, we propose a new product knowledge graph (PKG) embedding approach for learning the intrinsic product relations as product knowledge for e-commerce.
Self-attention with Functional Time Representation Learning
To bridge the gap between modelling time-independent and time-dependent event sequence, we introduce a functional feature map that embeds time span into high-dimensional spaces.
A Large-Scale Deep Architecture for Personalized Grocery Basket Recommendations
In this paper, we introduce a production within-basket grocery recommendation system, RTT2Vec, which generates real-time personalized product recommendations to supplement the user's current grocery basket.
Complementary-Similarity Learning using Quadruplet Network
We propose a novel learning framework to answer questions such as "if a user is purchasing a shirt, what other items will (s)he need with the shirt?"
Knowledge-aware Complementary Product Representation Learning
Standard usage of representation learning emphasizes on only one set of embedding, which is problematic for modelling such properties of complementariness.
Generative Graph Convolutional Network for Growing Graphs
Here we propose a unified generative graph convolutional network that learns node representations for all nodes adaptively in a generative model framework, by sampling graph generation sequences constructed from observed graph data.
Robust Counterfactual Inferences using Feature Learning and their Applications
From the randomized experiment, we learn the feature representations which divide the population into subpopulations where we observe statistically significant difference in average customer feedback between those who were subjected to the intervention and those who were not, with a level of significance l, where l is a configurable parameter in our model.
Continuously-adaptive discretization for message-passing algorithms
Continuously-Adaptive Discretization for Message-Passing (CAD-MP) is a new message-passing algorithm employing adaptive discretization.
