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Microsoft is excited to be a Silver sponsor of the 12th ACM International conference on Web Search and Data Mining. Come by our booth to chat with our experts, see demos of our latest research and find out about career opportunities with Microsoft.
Program Committee Co-Chair
Senior Program Committee Members
Program Committee Members
Ahmed Hassan Awadallah
Arnd Christian König
Task Intelligence Workshop
Tuesday, February 12, 2019
Attending to What Matters
Tuesday, February 12, 2019 | 10:00 AM–10:30 AM | Session 1: Search and Ranking
MSA: Jointly Detecting Drug Name and Adverse Drug Reaction Mentioning Tweets with Multi-Head Self-Attention
Tuesday, February 12, 2019 | 11:00 AM–12:30 PM | Session 2: Knowledge Graphs and Analytics
Clustered Monotone Transforms for Rating Factorization
Raghav Somani, Gaurush Hiranandani, Oluwasanmi Koyejo, Sreangsu Acharyya
Wednesday, February 13, 2019 | 10:00 AM–10:30 PM | Session 5: Understanding Conversation, Discussion, and Opinions
Multi-Representation Fusion Network for Multi-Turn Response Selection in Retrieval-Based Chatbots
Chongyang Tao, Wei Wu, Can Xu, Wenpeng Hu, Dongyan Zhao, Rui Yan
Attitude Detection for One-Round Conversation: Jointly Extracting Target-Polarity Pairs
Zhaohao Zeng, Ruihua Song, Pingping Lin, Tetsuya Sakai
Wednesday, February 13, 2019 | 4:15 PM-5:30 PM | Session 8: Counterfactual and Causal Learning
Genie: An Open Box Counterfactual Policy Estimator for Optimizing Sponsored Search Marketplace
Murat Ali Bayir, Mingsen Xu, Yaojia Zhu, Yifan Shi
Thursday, February 14, 2019 | 9:00 AM–10:30 AM | Session 9: Recommendation
*Best Paper Award
Himanshu Jain, Venkatesh Balasubramanian, Bhanu Teja Chunduri, Manik Varma
Neural Tensor Factorization for Temporal Interaction Learning
Xian Wu, Baoxu Shi, Yuxiao Dong, Chao Huang, Nitesh Chawla
Shaping Feedback Data in Recommender Systems with Interventions Based on Information Foraging Theory
Thursday, February 14, 2019 | 11:00 AM–11:45 AM | Session 10: Personalization and Characterizing User Behavior
Neural Demographic Prediction using Search Query
Thursday, February 14, 2019 | 2:00 PM–3:25 PM | Session 11: Domain Transfer and Representation Learning
Domain Adaptation for Commitment Detection in Email
Demos & Tutorials
clstk: The Cross-Lingual Summarization Tool-Kit
Tuesday, February 12 | 3:30 PM–4:15 PM
Nisarg Jhaveri, Manish Gupta, Vasudeva Varma
Causal Inference and Counterfactual Reasoning
As computing systems are more frequently and more actively intervening to improve people’s work and daily lives, it is critical to correctly predict and understand the causal effects of these interventions. This tutorial will introduce participants to concepts in causal inference and counterfactual reasoning, drawing from abroad literature from statistics, social sciences and machine learning. To tackle such questions, we will introduce the key ingredient that causal analysis depends on—counterfactual reasoning—and describe the two most popular frameworks based on Bayesian graphical models and potential outcomes. Based on this, we will cover a range of methods suitable for doing causal inference with large-scale online data, including randomized experiments, observational methods like matching and stratification, and natural experiment-based methods such as instrumental variables and regression discontinuity. We will also focus on best practices for evaluation and validation of causal inference techniques, drawing from our own experiences.
Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned
Monday, February 11 | 1:30 PM–5:00 PM
Sarah Bird, Krishnaram Kenthapadi, Emre Kiciman, Margaret Mitchell
Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine-learned models and data-driven systems, and the potential for such systems to discriminate against certain population groups, due to biases in algorithmic decision-making systems. This tutorial presents an overview of algorithmic bias/discrimination issues observed over the last few years and the lessons learned key regulations and laws, and evolution of techniques for achieving fairness in machine learning systems. We will motivate the need for adopting a “fairness by design” approach (as opposed to viewing algorithmic bias/fairness considerations as an afterthought) when developing machine learning based models and systems for different consumer and enterprise applications. Then, we will focus on the application of fairness-aware machine learning techniques in practice, by presenting non-proprietary case studies from different technology companies. Finally, based on our experiences working on fairness in machine learning at companies such as Facebook, Google, LinkedIn, and Microsoft, we will present open problems and research directions for the data mining/machine learning community.
A Case Study on Microsoft’s Ruuh.ai: Is User Growth a Peril to Research Progress?
Monday, February 11 | 11:00 AM–12:30 PM
Striking a balance between business goals such as user growth and deep meaningful research is always a challenging task in an industrial research setting. In this talk, taking Microsoft’s Ruuh as a case study, we will discuss the challenges and opportunities in the industry when it comes to research. Microsoft’s Ruuh was conceptualized about 2.5 years back and the main product promise of Ruuh is to be able to talk to its users on any subject they choose. We realized that the promise meant thinking beyond the utilitarian notion of merely generating “relevant” responses and enabling Ruuh to comprehend and meet a wider range of user social needs, like expressing happiness when user’s favorite team wins, sharing a cute comment on showing the pictures of the user’s pet and so on. At the onset, this seems an impossible task to achieve coupled with aggressive release deadline and pressure to grow usage. However, in this talk we will discuss how our research progress helped the user growth and vice versa, and also discuss scenarios where we suffered setbacks. A good quality product leads to high usage which in turn provides the much-needed data to improve the research and understand the flaws in the current approach. At the same time, high usage of the product forces the team to focus on the efficiency, cost per query and other infrastructure related workloads. This talk will take real-world examples and explain these tradeoffs. More details of the talk are presented in last section.
‘No Interaction’ as Indicator of Search Satisfaction: Accounting for Good Abandonment in User Success Metrics
Monday, February 11 | 11:00 AM–12:30 PM
At Bing, measuring user success has always been a deciding factor as to which feature or change is shipped to production. Testing such changes is carried out through randomized controlled experiments, where success metrics are used to measure the treatment effect on user satisfaction. Over the years, we have designed and refined our metrics to capture various user interactions, from search queries to clicks and hovers, and interpreted them to predict users’ satisfaction with the search engine. One of the main scenarios that is hard to interpret is search result page abandonment, where the user doesn’t click on the page or interact with any specific element. In this scenario of abandonment, we need to differentiate cases where the user abandoned due to getting the information they need without clicking on any results, from those where the user abandoned due to a defective and/or unsatisfactory search result page. In this talk, we outline Bing’s journey in addressing this measurement problem. We talk about our initial effort of considering the presence of specific elements on the page as indicator of success; to our offline/online hybrid approach to identify good abandonment; and finally, to a fully-online solution that relies on a user’s behavior across their search session. We also cover the pitfalls of the different approaches, how we evaluate them and the current challenges and problems left to solve.
Friday, February 15 | 9:00 AM–2:00 PM
Invited Speaker: Paul Bennett
Tasks are defined pieces of work that range in scope from specific (sending an email) to broad (planning a wedding) and are central to all aspects of information access and use. Task intelligence spans technologies and experiences to extract, understand, and support the completion of short- and long-term tasks. Helping users complete tasks is a key capability of search systems, digital assistants, and productivity applications and poses core challenges in data mining and knowledge representation and draws on additional research from areas such as machine learning and natural language processing. The workshop will comprise a mixture of research paper presentations, reports from data challenge participants, including system demonstrations if available.