| 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 |
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.
| No. | Title | Authors |
IR Models and Theories (I) |
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| 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 |
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| 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) |
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| 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) |
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| 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 |
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| 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 |