“New Learning Frameworks for Information Retrieval” is the title of Yisong Yue’s January 2011 Ph.D. dissertation at Cornell University. (Professor Thorsten Joachims, his Ph.D. adviser, flatteringly reviewed Surf Canyon in the past.) While the thesis generally “proposes principled approaches to formalize the learning problems for information retrieval, with an eye towards developing a united learning framework,” it specifically discusses dynamic search interfaces and the value of disambiguating intent and implicitly re-ranking results in real time.
In §4.6.1, entitled “Beyond Predicting Static Ranking,” Yisong writes (emphasis added):
“It is common for information retrieval research to focus either on relevance estimation or user interface design, but rarely both simultaneously. However, for many tasks, it can be useful to model both jointly… One major limitation of result diversification over static rankings is that it sacrifices recall in favor of some minimal amount of utility for all usage intents – such a limitation could be dealt with by moving towards more dynamic interfaces.
Consider the example interface shown in Figure 4.8, which is inspired by and adapted from the SurfCanyon.com search engine … by clicking or mousing over a result that matches the user’s intent, additional indented results are inserted into the original ranking… This interaction is quite natural, since the process resembles navigating a dropdown menu and since users are already familiar with result indentation. And yet even this one additional degree of freedom in content display can offer tremendous benefits…”
After the University College Dublin paper “A Recommender System Approach to Enhance Web Search and Query Formulation” and the Universidade Estadual de Maringá paper “An Approach to the Customization of Web Search Results,” this is the third academic reference to Surf Canyon. We’re delighted by the attention we’re receiving from the academic community.
Lastly, the team at Cornell recently drafted a brilliant paper, entitled “Dynamic Ranked Retrieval,” which dives deep into the study of real-time implicit ranking and offers statistical support for the “tremendous benefits” described above. It has been accepted for publication at WSDM 2011. While not yet public (we’ll post here when it is), we’ve been given permission to offer a sneak preview from the introduction (emphasis added):
“… most queries are ambiguous at some level. For such queries, there is often no single ranking that satisfies all users and query intents. While result diversification aims to provide a “compromise ranking” that provides some utility for all intents, diversification necessarily sacrifices recall…
The key idea is to make the ranking “dynamic” – namely, allowing it to change in response to user interactions after the query was issued.
From the user’s perspective, this may look as illustrated in Figure 1. This interface is inspired by and adapted from the SurfCanyon.com search engine…”
[Update 11/28/2010] The final version of “Dynamic Ranked Retrieval” is now available.
[Update 2/9/2011] The WSDM 2011 conference selected “Dynamic Ranked Retrieval” as one of six Best Paper Candidates.