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RIAO 2010 in Paris, France.

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The 9th International RIAO Conference has started in Paris, France (28-30 April, 2010). It is unfortunate that it is being held concurrently with WWW 2010 in Raleigh.
The first RIAO conference was held in Grenoble in 1985. RIAO is currently a triennial conference, addressing Information Retrieval research topics of interest to both Academia and Industry. This year, the conference focuses on Adaptivity, Personalization and Fusion of Heterogeneous Information.
The following papers have caught my eyes, while browsing the RIAO 2010 program:
  • Boiling down information retrieval test collections. T. Sakai et al. (Microsoft Research Asia, CMU)
  • Improving tag recommendation using social networks. A. Rae et al. (The Open University, Yahoo! Research Barcelona).
  • Analysis of robustness in trust-based recommender systems. Z. Cheng and N. Hurley (UCD)
  • Opinion-finding in blogs: A passage-based language modelling approach. M. Saad Missen et al (IRIT)
  • Predicting query performance using query, result, and user interaction features. Q. Guo et al. (Emory University/Microsoft Research)
  • Towards a collection-based results diversification. J.A. Akinyemi et al. (University of Waterloo)
In addition, the TerrierTeam has two full papers, which are being presented today at the conference (hopefully, the slides will follow shortly):
  • Voting for Related Entities by R.L.T. Santos, C. Macdonald and I. Ounis. The paper addresses the problem of entity search, where the goal is to rank not documents, but entities in response to a given query. The paper proposes to tackle this problem as a voting process, by considering the occurrence of an entity among the top ranked documents for a given query as a vote for the existence of a relationship between this and the entity in the query. The approach led to high precision and unparalleled recall compared to TREC 2009 systems.
  • News Article Ranking: Leveraging the Wisdom of Bloggers by R. McCreadie, C.Macdonald and I. Ounis. The paper investigates how news article ranking can be performed automatically, so as to assist editors in selecting the articles, which should make the front page of their newspaper. In particular, the paper investigates the blogosphere as a prime source of evidence, on the intuition that bloggers, and by extension their blog posts, can indicate interest in one news article or another. The paper proposes to model the automatic news article ranking task as a voting process, where each relevant blog post acts as a vote for one or more news articles. The approach led to the best TREC 2009 retrieval performance in the Blog track.
Craig Macdonald is tweeting the conference, pending an appropriate wireless signal. You can follow some bits of the RIAO conference through the #riao2010 hashtag.
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