Search Results for author:
Found 26 papers, 5 papers with code
Guidelines for releasing a variant effect predictor
Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering.
Thread Detection and Response Generation using Transformers with Prompt Optimisation
To address these challenges an end-to-end model that identifies threads and prioritises their response generation based on the importance was developed, involving a systematic decomposition of the problem into discrete components - thread detection, prioritisation, and performance optimisation which was meticulously analysed and optimised.
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.
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.
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.
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.
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.
Inductive Representation Learning on Temporal Graphs
Moreover, node and topological features can be temporal as well, whose patterns the node embeddings should also capture.
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.
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.
