Note: this post is cross-posted at BLOG@CACM.
Unfortunately, I woke up this morning rather under the weather, so I’m having to resort to remotely reporting on the second day of WSDM 2010 conference, based on the published proceedings and the tweet stream.
The day started with a keynote from Harvard economist Susan Athey. Her research focuses on the design of auction-based markets, a topic core to the business of search which largely relies on auction-based advertising models (cf. Google AdWords). Then came a session focused on learning and optimization. One paper proposed a method to learn ranking functions and query categorization simultaneously, reflecting that different categories of queries leads users to have different expectations about ranking. Another combined traditional list-based ranking with pair-wise comparisons between results to separate the results into tiers reflecting grades of relevance. An intriguing approach to query recommendation treated it as an optimization problem, perturbing users’ query-reformulation path to maximize the expected value of a utility function over the search session. Another paper looked not at ranking per se, but rather at improving the quality of training data for using machine learning for ranking. The final paper of the session, which earned a best-paper nomination, modeled document relevance based not on click-through behavior, but rather on post-click user behavior.
The next session was about users and measurement. It opened with another best-paper nominee: a analysis of over a hundred million users to understand how they re-find web content. Another offered a rigorous analysis of the often sloppily presented “long-tail” hypothesis: it found that light users disproportionately prefer content at the head of distribution while heavy users disproportionately prefer the tail. Another log-analysis paper analyzed search logs using a partially observable Markov model, a variant of thehidden Markov model in which not all of the hidden state transitions emit observable events–and compared the latent variables with eye-tracking studies. An intriguing study demonstrated that user behavior models are more predictive of goal success than models based on document relevance. The final paper of the session proposed methods for quantifying the reusability of the test collections that lie at the heart of information retrieval evaluation.
The last session of the day focused on social aspects of search. Two of the papers were concerned with modeling authority and influence in social networks, a problem in which I take a deep personal interest. Another inferred attributes of social network users based on those of other users in their communities (cg. MIT’s Project Gaydar). Another analyzed Flickr and Last.fm user logs to show that users’ semantic similarity based on their tagging behavior is predictive of social links. The final paper tackled the sparsity of social media tags by inferring latent topics from shared tags and spatial information.
Not surprisingly, a disproportionate number of contributors to the conference work at major web search companies, who have both the motivation to improve results and the access to data that is needed for such research. One of the ongoing research challenges for the field is to find ways to make this data available to others while respecting the business concerns of search engine companies and the privacy concerns of their users.