Search Results for author:
Found 16 papers, 5 papers with code
The Use of Generative Search Engines for Knowledge Work and Complex Tasks
Until recently, search engines were the predominant method for people to access online information.
Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models
Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems.
TnT-LLM: Text Mining at Scale with Large Language Models
Transforming unstructured text into structured and meaningful forms, organized by useful category labels, is a fundamental step in text mining for downstream analysis and application.
PEARL: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers
Powerful large language models have facilitated the development of writing assistants that promise to significantly improve the quality and efficiency of composition and communication.
S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking in the Era of LLMs
The traditional Dialogue State Tracking (DST) problem aims to track user preferences and intents in user-agent conversations.
Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies
However, using LLMs to generate a user intent taxonomy and apply it for log analysis can be problematic for two main reasons: (1) such a taxonomy is not externally validated; and (2) there may be an undesirable feedback loop.
Situating Recommender Systems in Practice: Towards Inductive Learning and Incremental Updates
With information systems becoming larger scale, recommendation systems are a topic of growing interest in machine learning research and industry.
Learning Causal Effects on Hypergraphs
Hypergraphs provide an effective abstraction for modeling multi-way group interactions among nodes, where each hyperedge can connect any number of nodes.
Learning Fair Node Representations with Graph Counterfactual Fairness
In this framework, we generate counterfactuals corresponding to perturbations on each node's and their neighbors' sensitive attributes.
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.
Addressing Marketing Bias in Product Recommendations
However, these interactions can be biased by how the product is marketed, for example due to the selection of a particular human model in a product image.
CosRec: 2D Convolutional Neural Networks for Sequential Recommendation
Sequential patterns play an important role in building modern recommender systems.
Fine-Grained Spoiler Detection from Large-Scale Review Corpora
This paper presents computational approaches for automatically detecting critical plot twists in reviews of media products.
Beyond "How may I help you?": Assisting Customer Service Agents with Proactive Responses
We study the problem of providing recommended responses to customer service agents in live-chat dialogue systems.
Recommendation Through Mixtures of Heterogeneous Item Relationships
Recommender Systems have proliferated as general-purpose approaches to model a wide variety of consumer interaction data.
Modeling Ambiguity, Subjectivity, and Diverging Viewpoints in Opinion Question Answering Systems
Product review websites provide an incredible lens into the wide variety of opinions and experiences of different people, and play a critical role in helping users discover products that match their personal needs and preferences.
