Conference Program
Keynote: Computational Advertising and Recommender Systems
Time: Saturday, October 25 2008, 9:00 am
Andrei Z. Broder
Fellow & VP Computational Advertising
Yahoo! Research
USA
Computational advertising is an emerging scientific sub-discipline, at the intersection of large scale search and text analysis, information retrieval, statistical modeling, machine learning, optimization, and microeconomics. The central challenge of computational advertising is to find the "best match" between a given user in a given context and a suitable advertisement. The context could be a user entering a query in a search engine ("sponsored search"), a user reading a web page ("content match" and "display ads"), a user conversing on a cell phone ("mobile advertising"), and so on. The information about the user can vary from scarily detailed to practically nil. The number of potential advertisements might be in the billions. Thus, depending on the definition of "best match" this challenge leads to a variety of massive optimization and search problems, with complicated constraints.
The main part of this talk will give an introduction to computational advertising and present some illustrative research. In the second part we will discuss connections to recommender systems and present a couple of open problems of potential interest to both communities.
Short bio of speaker: Andrei Broder is a Fellow and Vice President for Computational Advertising in Yahoo! Research. Previously he was an IBM Distinguished Engineer and the CTO of the Institute for Search and Text Analysis in IBM Research. From 1999 until 2002 he was Vice President for Research and Chief Scientist at the AltaVista Company. He was graduated Summa cum Laude from Technion, the Israeli Institute of Technology, and obtained his M.Sc. and Ph.D. in Computer Science at Stanford University under Don Knuth. His current research interests are centered around computational advertising, web search, context-driven information supply, and randomized algorithms. Broder is co-winner of the Best Paper award at WWW6 (for his work on duplicate elimination of web pages) and at WWW9 (for his work on mapping the web). He has authored more than eighty papers and was awarded twenty-five patents. He is an ACM Fellow, an IEEE fellow, and past chair of the IEEE Technical Committee on Mathematical Foundations of Computing.
Program Highlights
| Thursday, October 23 Tutorials | ||
| Place: |
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| 10:00-12:00 pm | Tutorial 1: Robust Recommender Systems - Robin Burke | |
| 12:00-01:30 pm | Lunch (not included). | Recommended: Cafeteria of BC building (on fourth floor of same BC building). Map of all the restaurants at EPFL |
| 01:30-03:30 pm | Tutorial 2: Recent Progress in Collaborative Filtering - Yehuda Koren | |
| Break | ||
| 04:00-06:00 pm | Tutorial 3: Context-Aware Recommender Systems - Gedas Adomavicius and Alex Tuzhilin | |
| 06:30 pm | Opening Reception. | At Polydome |
| Friday, October 24 | ||
| Place: Polydôme (map), EPFL | ||
| 07:30 am | Welcome Coffee and Croissant (Reception desk will be open at 07:30 am) | |
| 08:30 am | Conference Opening | |
| 08:45 am | Paper Session 1 (5 papers): Recommendation Algorithms | |
| Shengchao Ding, Shiwan Zhao, Quan Yuan, Xiatian Zhang, Rongyao Fu and Lawrence Bergman. - Boosting Collaborative Filtering Based on Statistical Prediction Errors | ||
| Yoon-Joo Park and Alexander Tuzhilin. - The Long Tail of Recommender Systems and How to Leverage It | ||
| Asela Gunawardana and Chris Meek. - Tied Boltzmann Machines for Cold Start Recommendations | ||
| Rossano Schifanella, AndrÈ Panisson, Cristina Gena and Giancarlo Ruffo. - MobHinter: Epidemic Collaborative Filtering and Self-Organization in Mobile Ad-Hoc Networks | ||
| Guy Shani, Chris Meek and Max Chickering. - Mining Recommendations From The Web | ||
| 10:30 am | Break | |
| 11:00 am | Paper Session 2 (4 papers): Social Networks and Recommenders | |
| Panagiotis Symeonidis, Alexandros Nanopoulos and Yannis Manolopoulos. - Tag Recommendations based on Tensor Dimensionality Reduction | ||
| Valentina Zanardi and Licia Capra. - Social Ranking: Uncovering Relevant Content Using Tag-based Recommender Systems | ||
| Werner Geyer, Casey Dugan, David Millen, Michael Muller and Jill Freyne. - Recommending Topics for Self-Descriptions in Online User Profiles | ||
| Nikhil Garg and Ingmar Weber. - Personalized, Interactive Tag Recommendation for Flickr | ||
| 12:30 pm | Lunch (not included) |
Recommended: Parmentier (map), EPFL Map of all the restaurants at EPFL |
| 01:45 pm | Social Recommenders for the Masses : Moderator: MG Siegler, Venture Beat |
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| 03:00 pm | Strands presentations | |
| 04:15 pm | Break | |
| 04:45 pm | Paper Session 3 (3 papers): User Studies | |
| Li Chen and Pearl Pu. - A Cross-Cultural User Evaluation of Product Recommender Interfaces | ||
| Songhua Xu, Hao Jiang, Francis Lau and Yunhe Pan. - Personalized Online Document, Image and Video Recommendation via Commodity Eye-tracking | ||
| Veronica Maidel, Peretz Shoval, Bracha Shapira and Meirav Taieb-Maimon. - Evaluation of an Ontology-Content Based Filtering Method for a Personalized Newspaper | ||
| 06:15 pm | One-time Bus leaves from Avenue Piccard, in front of Polydome to Movenpick hotel and then the Lausanne Olympic Museum. | |
| 07:00 pm | Conference Dinner. (included) Finalists for the Strands prize: |
The gala dinner features a Cocktail reception until 7:30pm, during which a tour of the museum is highly recommended. This is followed by a sit-down hot buffet dinner (food, drink and dessert are served between 7:30 until 9pm; menu is here). Strands Award finalist will be announced during coffee and dessert. Museum is open for the participants until midnight. |
| Saturday, October 25 | ||
| Place: Polydôme (map), EPFL | ||
| 09:00 am | Keynote: Andrei Broder - Computational Advertising and Recommender Systems | |
| 10:00 am | Poster Session (including coffee break) | |
| 12:30 pm | Lunch (including PC meeting). | Included: Parmentier (map), EPFL. |
| 01:45 pm | Plug-and-Play Recommenders : Moderator: Benjamin Dias, Unilever |
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| 03:00 pm | Paper Session 4 (3 papers): Conversational Systems | |
| Hu Wu, Dong Zhang, Yongji Wang and Xiang Cheng. - Incremental Probabilistic Latent Semantic Analysis for Automatic Question Recommendation | ||
| Haoyuan Li, Yi Wang, Dong Zhang, Ming Zhang and Edward Chang. - PFP: Parallel FP-Growth for Query Recommendation | ||
| Tarik Hadzic and Barry O'Sullivan. - Critique Graphs for Catalogue Navigation | ||
| 04:00 pm | Break | |
| 04:30 pm | Paper Session 5 (5 papers): Recommender Challenges | |
| Mi Zhang and Neil Hurley. - Avoiding Monotony: Improving the Diversity of Recommendation Lists | ||
| Hilmi Yildirim and Mukkai S. Krishnamoorthy. - A Random Walk Method for Alleviating the Sparsity Problem in Collaborative Filtering | ||
| Markus Zanker. - A collaborative constraint-based meta-level recommender | ||
| Paul Resnick and Rahul Sami. - The Information Cost of Manipulation-Resistance in Recommender Systems | ||
| Kenneth Bryan, Michael O'Mahony and Padraig Cunningham. - Unsupervised Retrieval of Attack Profiles in Collaborative Recommender Systems | ||
| 06:15 pm | Conference Ends | |
| Sunday, October 26 Doctoral Consortium | ||
| Place: BC 410 (map), EPFL. | Lunch and coffee breaks: Included. BC 4th floor. | |
List of Accepted Papers
Long Papers – Oral Presentation
1. Panagiotis Symeonidis, Alexandros Nanopoulos and Yannis Manolopoulos. Tag Recommendations based on Tensor Dimensionality Reduction
2. Shengchao Ding, Shiwan Zhao, Quan Yuan, Xiatian Zhang, Rongyao Fu and Lawrence Bergman. Boosting Collaborative Filtering Based on Statistical Prediction Errors
3. Li Chen and Pearl Pu. A Cross-Cultural User Evaluation of Product Recommender Interfaces
4. Valentina Zanardi and Licia Capra. Social Ranking: Uncovering Relevant Content Using Tag-based Recommender Systems
5. Kenneth Bryan, Michael O'Mahony and Padraig Cunningham. Unsupervised Retrieval of Attack Profiles in Collaborative Recommender Systems
6. Mi Zhang and Neil Hurley. Avoiding Monotony: Improving the Diversity of Recommendation Lists
7. Paul Resnick and Rahul Sami. The Information Cost of Manipulation-Resistance in Recommender Systems
8. Guy Shani, Chris Meek and Max Chickering. Mining Recommendations From The Web
9. Hu Wu, Dong Zhang, Yongji Wang and Xiang Cheng. Incremental Probabilistic Latent Semantic Analysis for Automatic Question Recommendation
10. Werner Geyer, Casey Dugan, David Millen, Michael Muller and Jill Freyne. Recommending Topics for Self-Descriptions in Online User Profiles
11. Haoyuan Li, Yi Wang, Dong Zhang, Ming Zhang and Edward Chang. PFP: Parallel FP-Growth for Query Recommendation
12. Tarik Hadzic and Barry O'Sullivan. Critique Graphs for Catalogue Navigation
13. Veronica Maidel, Peretz Shoval, Bracha Shapira and Meirav Taieb-Maimon. Evaluation of an Ontology-Content Based Filtering Method for a Personalized Newspaper
14. Rossano Schifanella, André Panisson, Cristina Gena and Giancarlo Ruffo. MobHinter: Epidemic Collaborative Filtering and Self-Organization in Mobile Ad-Hoc Networks
15. Nikhil Garg and Ingmar Weber. Personalized, Interactive Tag Recommendation for Flickr
16. Markus Zanker. A collaborative constraint-based meta-level recommender
17. Songhua Xu, Hao Jiang, Francis Lau and Yunhe Pan. Personalized Online Document, Image and Video Recommendation via Commodity Eye-tracking
18. Asela Gunawardana and Chris Meek. Tied Boltzmann Machines for Cold Start Recommendations
19. Yoon-Joo Park and Alexander Tuzhilin. The Long Tail of Recommender Systems and How to Leverage It
20. Hilmi Yildirim and Mukkai S. Krishnamoorthy. A Random Walk Method for Alleviating the Sparsity Problem in Collaborative Filtering
Long Papers – Poster Presentation
1. Neal Lathia, Stephen Hailes and Licia Capra. kNN CF: A Temporal Social Network
2. Steffen Rendle and Lars Schmidt-Thieme. Online-Updating Regularized Kernel Matrix Factorization Models for Large-Scale Recommender Systems
3. Martijn Kagie, Michiel van Wezel and Patrick J.F. Groenen. Choosing Attribute Weights for Item Dissimilarity using Clickstream Data with an Application to a Product Catalog Map
4. Pierpaolo Basile, Marco de Gemmis, Pasquale Lops and Giovanni Semeraro. Integrating Tags in a Semantic Content-based Recommender
5. Vinod Krishnan, Pradeep Narayanashetty, Mukesh Nathan, Richard Davies and Joseph Konstan. Who Predicts Better? – Results from an Online Study Comparing Humans and an Online Recommender System
6. Markus Weimer, Alexandros Karatzoglou and Alex Smola. Adaptive Collaborative Filtering
7. Alexander Brodsky, Sylvia Henshaw and Jon Whittle. CARD: A Decision-Guidance Framework and Application for Recommending Composite Alternatives
8. Kleanthi Lakiotaki and Nikolaos Matsatsinis. UTA-Rec: A Recommender System based on Multiple Criteria Analysis
9. Oscar Celma and Perfecto Herrera. Evaluating the quality of novel recommendations
10. Juan Recio-Garcia, Belen Diaz-Agudo and Pedro González Calero. Prototyping Recommender Systems in jCOLIBRI
11. Nathan Oostendorp and Paul Resnick. Three Recommender Approaches to Interface Controls Reduction
12. Georgia Koutrika, Robert Ikeda, Benjamin Bercovitz and Hector Garcia-Molina. Flexible Recommendations over Rich Data
13. Gabor Takacs, Istvan Pilaszy, Domonkos Tikk and Bottyan Nemeth. Matrix Factorization and Neighbor Based Algorithms for the Netflix Prize Problem
14. Andriy Shepitsen, Jonathan Gemmell, Bamshad Mobasher and Robin Burke. Personalized Recommendation in Collaborative Tagging Systems Using Hierarchical Clustering
15. Sara Drenner, Shilad Sen and Loren Terveen. Crafting Initial User Experience to Achieve Community Goals
Short Papers – Poster Presentation
1. Toine Bogers and Antal van den Bosch. Recommending Scientific Articles Using CiteULike
2. Arun Agrahri, divya anand and John Riedl. Can People Collaborate To Improve The Relevance Of Search Results?
3. M. Benjamin Dias, Dominique Locher, Ming Li, Wael El-Deredy and Paulo Lisboa. The Value of Personalised Recommender Systems to e-Business: A Case Study
