Preliminary Program

	Tue 29 Nov	Wed 30 Nov	Thu 1 Dec	Fri 2 Dec
8.00 - 9.00		Registration Open	Registration Open	Registration Open
9.00 - 9.15		Welcome to AIRS 2016
9.15 - 10.15		Keynote 1 (Kam-Fai WONG): NLP for Microblog Summarization	Keynote 2 (Hang LI): Will Question Answering Become the Main Theme of IR Research?	Keynote 3 (Emine Yilmaz): New Ways of Thinking about Search with New Devices
10.15 - 10.45		Coffee Break	Coffee Break	Coffee Break
10.45 - 12.00		Session 1: IR Models and Theories (I)	Session 4: IR Models and Theories (II)	Session 6: Personalization and Recommendation (II)
12.00 - 14.00		Lunch Break	Lunch Break	Lunch Break
14.00 - 15.15	Registration Open	Session 2: Machine Learning and Data Mining for IR	Session 5: Personalization and Recommendation (I)	Session 7: IR evaluation
15.15 - 15.45		Coffee Break	Coffee Break	Closing
15.45 - 17.00		Session 3: IR applications and User modeling	Poster & Demo Session
18.30 - 20.30	Welcome Reception (18:00 to 20:00)		Conference Banquet

Conference Venue

• All full paper presentation sessions will be in Lecture Hall, 2nd floor of the FIT Building, Tsinghua University campus, Beijing, China.
• All tea beaks, lunches, and poster session will be near to the door of Lecture Hall.
• We will have a reception in the evening of 29 November (Tuesday), from 18:30 to 20:30, in the Sunshine Hall of THU Science Park
• The banquet will be held in Xihe Yayuan Beijing Roast duck Restaurant. Participants will take shuttle bus together at the front door of FIT Building at 6:00PM.

Accepted Papers and Posters

No.	Title	Authors
IR Models and Theories (I)
1	Modeling Relevance as a Function of Retrieval Rank	Xiaolu Lu, Alistair Moffat and Shane Culpepper
2	The Effect of Score Standardisation on Topic Set Size Design	Tetsuya Sakai
3	Incorporating Semantic Knowledge into Latent Matching Model in Search	Shuxin Wang, Xin Jiang, Hang Li, Jun Xu, and Bin Wang
IR Models and Theories (II)
4	Keyqueries for Clustering and Labeling	Tim Gollub, Matthias Busse, Benno Stein and Matthias Hagen
5	A Comparative Study of Answer-contained Snippets and Traditional Snippets	Xian-Ling Mao, Dan Wang, Yi-Jing Hao, Wenqing Yuan and Heyan Huang
6	Local Community Detection via Edge Weighting	Weiji Zhao, Fengbin Zhang and Jinglian Liu
Machine Learning and Data Mining for IR
7	Learning a Semantic Space of Web Search via Session Data	Lidong Bing, Zhengyu Niu, Wai Lam and Haifeng Wang
8	TLINE: Scalable Transductive Network Embedding	Xia Zhang, Weizheng Chen and Hongfei Yan
9	Detecting Synonymous Predicates from Online Encyclopedia with Rich Features	Zhe Han, Yansong Feng and Dongyan Zhao
IR applications and User modeling
10	Patent Retrieval Based on Multiple Information Resources	Kan Xu, Hongfei Lin, Yuan Lin, Bo Xu, Liang Yang and Shaowu Zhang
11	Simulating Ideal and Average Users	Matthias Hagen, Maximilian Michel and Benno Stein
12	Constraining Word Embeddings by Prior Knowledge – Application to Medical Information Retrieval	Xiaojie Liu, Jian-Yun Nie and Alessandro Sordoni
Personalization and Recommendation (I)
13	Use of Microblog Behavior Data in a Language Modeling Framework to Enhance Web Search Personalization	Arjumand Younus
14	A Joint Framework for Collaborative Filtering and Metric Learning	Tak-Lam Wong, Wai Lam, Haoran Xie and Fu Lee Wang
15	Scrutinizing Mobile App Recommendation: Identifying Important App-related Indicators	Jovian Lin, Kazunari Sugiyama, Min-Yen Kan and Tat-Seng Chua
Personalization and Recommendation (II)
16	User Model Enrichment for Venue Recommendation	Mohammad Aliannejadi, Ida Mele and Fabio Crestani
17	Learning Distributed Representation for Recommender Systems with a Network Embedding Approach	Wayne Xin Zhao, Jin Huang and Ji-Rong Wen
18	Factorizing Sequential and Historical Purchase Data for Basket Recommendation	Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu and Xueqi Cheng
IR evaluation
19	Search Success Evaluation with Translation Model	Cheng Luo, Yiqun Liu, Min Zhang and Shaoping Ma
20	Evaluating the Social Acceptability of Voice Based Smartwatch Search	Christos Efthymiou and Martin Halvey
21	How Precise Does Document Scoring Need To Be?	Ziying Yang, Alistair Moffat and Andrew Turpin
Short Paper
1	Noise Correction in Pairwise Document Preferences for Learning to Rank	Harsh Trivedi and Prasenjit Majumder
2	Table Topic Models for Hidden Unit Estimation	Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita
3	Query Subtopic Mining Exploiting Word Embedding for Search Result Diversification	Md Zia Ullah, Md Shajalal, Abu Nowshed Chy and Masaki Aono
4	Assessing the Authors of Online Books in Digital Libraries using Users Affinity	Baptiste De La Robertie
5	Reformulate or Quit: Predicting User Abandonment in Ideal Sessions	Mustafa Zengin and Ben Carterette
6	Learning to Rank with Likelihood Loss Functions	Yuan Lin, Liang Yang, Bo Xu, Hongfei Lin and Kan Xu
7	Learning to Improve Affinity Ranking for Diversity Search	Yue Wu, Jingfei Li, Peng Zhang and Dawei Song
8	An In-depth Study of Implicit Search Result Diversification	Hai-Tao Yu, Adam Jatowt, Roi Blanco, Hideo Joho, Joemon Jose, Long Chen and Fajie Yuan
9	Predicting Information Diffusion in Social Networks with Users' Social Roles and Topic Interests	Xiaoxuan Ren and Yan Zhang
10	When MetaMap meets Social Media in Healthcare: Are the Word Labels Correct?	Hongkui Tu, Zongyang Ma, Aixin Sun and Xiaodong Wang
11	Evaluation with Confusable Ground Truth	Jiyi Li and Masatoshi Yoshikawa