The Third Workshop for NExT++

Transcription

1 NEXT SEARCH CENTRE 下一代搜索技术联合研究中心 a NUS-Tsinghua joint centre on extreme search The Third Workshop for NExT++ CHUA Tat-Seng/ Sun Maosong/ Wendy Hall SINGAPORE 16 May 2018

2 NExT++: NUS-Tsinghua Centre on Extreme Search o Research on Big Unstructured data Analytics with Applications in Wellness, Fintech and Smart Nation o We are among the first to look into this topic in 2010 o Phase I: May 2010 to Sep 2016 with a Grant of S$11 million Emphasis: Technology for unstructured data analytics o Phase II: Oct 2016 to Sep 2021 with a Grant of S$12 million Emphasis : Deep unstructured data analytics o Additional Collaborator: Southampton University o With active participation of over 15 professors, over 30 PhD students, and 10 full-time researchers

3 We focus on unstructured data analytics o Two key challenges: big data and paradigm change Big Data Challenges: 1) Big Data Wellness Analytics 2) Multimodal KG & Chabot 3) Fintech 4) Rich Media Analytics 5) Recommendation Paradigm Change Challenges: 1) From Video to 3D and VR 2) From Recommendation to Influence

4 Applications: o Wellness, Fintech & Smart Nation Deliverables: o Software Infrastructures to help nurture and incubate new enterprises o Work with industries o New enterprises

5 USERS Analytics Personalization Apps Pregnancy Diabetes Depressions, etc. Knowledge Wellness Knowledge User Data Food Intake & Habits Activities (Physical & Cyber) Others: Sensors for Vital Signs, Test; Environment Data

6 Other related research done at Tsinghua: Depression modeling and monitoring Sleep disorder Intelligent remote medical examination system and deployment Southampton: Lifestyle interventions for Asthma Patients:

7 2) Multi-modal Knowledge Graph (MMKG) Research on fundamental building blocks towards MMKG Basic research on triplet extraction from text and video Research on bridging text and knowledge for joint representation of words and entities Building MMKG in fashion and food/wellness domains

8 Conversation systems: Task oriented Chabot and Chabot with emotion Multimodal Chabot: Research on capturing multimodal semantics, generating responses based on conversation history and domain knowledge, and reinforcement learning to further optimize the model

9 Leverage on alternate big data for Futures and Commodity price forecasting Related research done at Tsinghua: Fundamental and technical analysis of the future markets Network analysis for Fintech

10 Pipeline for end to end DL development cycles for Data Scientists labelling management Dataset management Training Task management model management Experiment management How to augment data to improve classifiers? Traditional: data crawling, data cropping, flipping New: domain transferring, data generation Our research: conditional data generation Generate data to improve model training esp. for fine grained classification tasks

11 User behaviors are affiliated with rich side information: User demographics; Item attributes Textual reviews; Various contexts Shallow methods like MF (matrix factorization) can t incorporate them well. [Zhang et al KDD 16] 1 ST Enhancement: Neural Factorization Machine (NFM) which can automatically learn: 1) Higher-order feature interactions implicitly 2) Second-order feature interactions explicitly 3) Feature representations (no manual efforts) Used by Alibaba for search ads ranking. [He and Chua, SIGIR 17]

12 2 nd Enhancement: Tree-enhanced Embedding Model NFM and others are black-box in learning higher-order interactions. We learn explainable rules with trees and integrate into neural model. Provide sound & explainable reasons (rules) on why the product is suitable choice for the user. Recommendation reason: User being <User-City: Florida, User-Style: Nightlife Seeker Luxury Traveler, User-Age: 50-64> would like to visit Item being <Item- Price: $$$$, Item-Attr: Foie Gras, Lobster> Embed the explainable rules on certain features from decision trees [Wang et al, WWW 18]

13 This phase focuses on bringing insights & intelligence to the systems: o Let machine handles big data, while human do creative tasks o Wide range of real-time unstructured big data analytics o Applications in wellness, Fintech, and smart nation... o Towards large-scale systems research, with deployment and collaborate with industries We have initiated several collaborations with industries on research in Fintech, wellness and e-commerce Look forward to suggestion and collaboration on next phases of research

14 THANKS Visit our Web Observatory: