KDD Cup 2024 Workshop:

Introduction by organizers.  
Moderator: Yilun Jin, Ph.D. Student at Hong Kong University of Science and Technology
Speaker: Dr. Zheng Li, Senior Applied Scientist at Amazon

Professor Huan Liu
Regents Professor, Arizona State University

Title: Social Media Mining: Embracing LLMs and Beyond
Abstract: Social media data differs from conventional data in many ways. It is big, noisy, linked, multimodal, and user generated. Through the lens of social media, unprecedented opportunities emerge for researchers in knowledge discovery and data mining. The prevalent usage of Large Language Models (LLMs) has added new challenges. We will use some of the challenges to illustrate the immediate need for (1) pondering what we should do to make LLMs more trustworthy and usable, and (2) contemplating a future beyond LLMs.

Professor Jingbo Shang
Associate Professor, University of California, San Diego

Title: Incubating Text Classifiers Following User Instruction with Nothing but LLM
Abstract: Automated knowledge extraction and discovery methods can address the diverse needs of different users. A fundamental open problem is how much user effort automated methods require to obtain useful knowledge. My research introduces a novel paradigm, extremely weak supervision (XWS), aimed at minimizing user effort. XWS involves only brief natural-language input from users to define tasks, such as a list of topics for classifying news articles, akin to the guidelines given to human annotators. In this talk, using text classification as an example, we will present a series of XWS methods. These methods encompass three primary approaches to pseudo-labeling: (1) mining-based X-Class, (2) generation-based Incubator, and (3) a hybrid approach, Text Grafting.

• Team: Team_NVIDIA
  Title: Winning Amazon KDD Cup'24
  Time: 14:00 - 14:25 Slides

• Team: AML_LabCityU
  Title: Second Place Overall Solution for Amazon KDD Cup 2024
  Time: 14:25 - 14:50 Slides

• Team: shimmering_as_the_stars
  Title: LLaSA: Large Language and E-Commerce Shopping Assistant
  Time: 14:50 - 15:00 Video


• Team: BMI_DLUT
  Title: Enhancing User Behavior Alignment by Input-Level Model Cooperation and Model-Level Parameter Optimization
  Time: 15:00 - 15:15 Slides


• Team: ZJU_AI4H
  Title: EC-Guide: A Comprehensive E-Commerce Guide for Instruction Tuning and Quantization by ZJU-AI4H
  Time: 15:15 - 15:25 Video Slides


• Team: Innova
  Title: Fine-Tuning Large Language Models for Multitasking in Online Shopping Using Synthetic Data
  Time: 15:25 - 15:40 Slides


• Team: CM_RLLM
  Title: Tailoring LLMs for Online Shopping: A Strategy for Multi-Task Learning and Domain-Specific Performance Enhancement
  Time: 15:40 - 15:50 Video

• Papers: OpenReview
• Posters: Check here