Learn-IR 2018 Workshop

WSDM 2018 Workshop

Learn-IR: Learning from User Interactions

Overview

While users interact with online services (e.g. search engines, recommender systems, conversational agents), they leave behind fine grained traces of interaction patterns. The ability to understand user behavior, record and interpret user interaction signals, gauge user satisfaction and incorporate user feedback gives online systems a vast treasure trove of insights for improvement and experimentation. More generally, the ability to learn from user interactions promises pathways for solving a number of problems and improving user engagement and satisfaction.

Understanding and learning from user interactions involves a number of different aspects - from understanding user intent and tasks, to developing user models and personalization services. A user's understanding of their need and the overall task develop as they interact with the system. Supporting the various stages of the task involves many aspects of the system, e.g. interface features, presentation of information, retrieving and ranking. Often, online systems are not specifically designed to support users in successfully accomplishing the tasks which motivated them to interact with the system in the first place. Beyond understanding user needs, learning from user interactions involves developing the right metrics and expiermentation systems, understanding user interaction processes, their usage context and designing interfaces capable of helping users.

Learning from user interactions becomes more important as new and novel ways of user interactions surface. There is a gradual shift towards searching and presenting the information in a conversational form. Chatbots, personal assistants in our phones and eyes-free devices are being used increasingly more for different purposes, including information retrieval and exploration. With improved speech recognition and information retrieval systems, more and more users are increasingly relying on such digital assistants to fulfill their information needs and complete their tasks. Such systems rely heavily on quickly learnig from past interactions and incorporating implicit feedback signals into their models for rapid development.


Topics

Learning from User Interactions will be a highly interactive full day workshop that will provide a forum for academic and industrial researchers working at the intersection of user understanding, search tasks, conversational IR and user interactions. The purpose is to provide an opportunity for people to present new work and early results, brainstorm different use cases, share best practices, and discuss the main challenges facing this line of research.


  • User Needs & Tasks Understanding:
    • User intent analysis/prediction
    • User goals & missions
    • Task identification
    • Task aware suggestions & recommendations

  • User Modeling & Personalization:
    • Short and Long-term User Modelling
    • Personalization
    • Diversification
    • Coherence

  • Metrics and Evaluation :
    • Metrics based on user interactions
    • User engagement metrics design
    • Evaluation mechanisms
    • User satisfaction prediction
    • Controlled laboratory study
    • Online metrics
    • Test collection

  • User Interaction Processes & Context :
    • User Journey Optimization
    • Evolution of search process
    • Stages of user interactions
    • User journey through the system
    • Leveraging contextual signals
    • Learning for user interaction optimization: algorithms, frameworks & system designs

  • Intelligent interface designs:
    • Adaptive personal digital assistants
    • Tailored decision support
    • Adaptive collaboration support

  • Applications:
    • Conversational search, chatbots, digital assistants
    • Contextual Advertising
    • E-commerce recommendations
    • Customer Support
    • Intelligent interfaces
    • Personal search
    • Case studies of real world implementations


Keynote Speakers

TBA

Important Dates

  • Submission Deadline: 30th November 2017 8th December 2017 (23:59 AOE Time)
  • Notification: 15th December 2017 23rd December 2017
  • Workshop: 9th February 2018

Submission

All workshop submissions must be formatted according to ACM SIG Proceedings template. Please feel free to include author names & affiliations in the submissions. We welcome submissions in either long or short format spanning 4-6 pages.

Authors should submit original papers in PDF format through the Easychair system.

This is a workshop where discussion is central, and all attendees are active participants. The workshop will include keynote talks to set the stage and ensure all attendees are on the same page. A small number of contributed papers will be selected for short oral presentation (15-10 minutes), all other papers have a 2 minute boaster, and all papers are presented as poster in an interactive poster session.

The results will be disseminated in various ways:

  • A high quality, peer reviewed workshop proceedings, published in the http://ceur-ws.org/ workshop proceedings series.
  • A report on the results of the workshop in the ACM SIGIR Forum of June 2018.
  • If the outcome lives up to our high expectations, we will consider a special issue in an appropriate journal.


Accepted Papers

  • Mixture-of-tastes Models for Representing Users with Diverse Interests. Maciej Kula
  • Counterfactual Learning-to-Rank for Optimizing DCG. Aman Agarwal and Thorsten Joachims
  • Understanding and Predicting User Satisfaction in Image Search. Zhijing Wu, Yiqun Liu, Min Zhang and Shaoping Ma
  • Fully Automated QA System for Large Scale Search and Recommendation Engines Leveraging Implicit User Feedback. Khalifeh Aljadda, Mohammed Korayem and Trey Grainger
  • Personal User Experience: The Needs of the Elderly and Intelligent Interfaces Using Data Analysis Techniques. Seungho Chae, Yoonsik Yang, Hyocheol Ro and Tack-Don Han
  • Deriving Tourist Mobility Patterns from Check-in Data. Linus W. Dietz, Daniel Herzog and Wolfgang W├Ârndl


Program

TBA

Organizers

  1. Rishabh Mehrotra (Spotify Research; University College London)

  2. Emine Yilmaz (University College London; Alan Turing Institute)

  3. Ahmed Hassan Awadallah (Microsoft Research)

You can contact us at learnIRwrkshp@gmail.com.

Steering Committee:

- Milad Shokouhi (Microsoft)
- Fernando Diaz (Spotify)
- Filip Radlinski (Google Research)
- Evangelos Kanoulas (University of Amsterdam)

Last updated: 3rd Jan 2018