Nowadays, intelligent information systems, especially the interactive information systems (conversational interaction systems; news feed recommender systems, and interactive search engines, etc.), are ubiquitous in real-world applications. These systems either converse with users explicitly through natural languages, or mine users interests and respond to users requests implicitly. Interactivity has become a crucial element towards intelligent information systems. Despite the fact that interactive information systems have gained significant progress, there are still many challenges to be addressed when applying these models to real-world scenarios. This half day workshop explores challenges and potential research, development, and application directions in applied interactive information systems. We aim to discuss the issues of applying interactive information models to production systems, as well as to shed some light on the fundamental characteristics, i.e., interactivity and applicability, of different interactive tasks. We welcome practical, theoretical, experimental, and methodological studies that advances the interactivity towards intelligent information systems. The workshop aims to bring together a diverse set of practitioners and researchers interested in investigating the interaction between human and information systems to develop more intelligent information systems.

Schedule (Beijing Time)

Jul 30 08:30-08:35 Opening
Jul 30 08:35-09:35 Keynote (Grounded Text Generation for Robust Conversational AI)
Jul 30 09:35-10:35 Keynote (Recent Research on Conversational Recommender System)
Jul 30 10:35-12:00 Paper presentations

Videos can be downloaded from here.


Jianfeng Gao

Microsoft Research
Title: Grounded Text Generation for Robust Conversational AI
Abstract: In this talk, I present a hybrid approach based on a Grounded Text Generation (GTG) model to building robust task bots at scale. GTG is a hybrid model which uses a large-scale Transform neural network as its backbone, combined with symbol manipulation modules for knowledge base inference and prior knowledge encoding, to generate responses grounded in dialog belief state and real-world knowledge for task completion. GTG is pre-trained on large amounts of raw text and human conversational data, and can be fine-tuned to complete a wide range of tasks. The hybrid approach and its variants are being developed simultaneously by multiple research teams. The primary results reported on task-oriented dialog benchmarks are very promising, demonstrating big potential of this approach. I provide an overview of this progress and discuss related methods and technologies that can be incorporated for building robust conversational AI systems.

Xiangnan He

University of Science and Technology of China
Title: Recent Research on Conversational Recommender System
Abstract: Recommender system serves users in an interactive manner by nature. However, most existing research has solved it as a prediction problem on static data and ignored the interactive nature. Such a static paradigm of recommendation has intrinsic limitations: it cannot solicit a user’s current preferences explicitly, and cannot identify accurate reasons as to why a user likes/dislikes certain item. We believe recommender systems should embrace conversational technologies to obtain user preferences explicitly and dynamically, and to overcome inherent limitations of their static models. In this talk, I will introduce our recent work on WSDM 2020 and KDD 2020 that formulate and address the conversational recommendation issue, providing a new direction orthogonal to traditional recommendation research.

Paper Presentations


Hongshen Chen

Research Scientist at Data Science Lab,

Zhaochun Ren

Professor at Shandong University.

Pengjie Ren

Postdoctoral researcher at the Information and Language Processing Systems (ILPS) group, University of Amsterdam.

Dawei Yin

Director of Search Science at Baidu.

Xiaodong He

Vice President of Technology of JD.COM Inc., Deputy Managing Director of JD AI Research, and Head of the Deep learning, NLP and Speech Lab