Paper Info
Title
Structured learning of two-level dynamic rankings
Abstract
For ambiguous queries, conventional retrieval systems are bound by two conflicting goals. On the one hand, they should diversify and strive to present results for as many query intents as possible. On the other hand, they should provide depth for each intent by displaying more than a single result. Since both diversity and depth cannot be achieved simultaneously in the conventional static retrieval model, we propose a new dynamic ranking approach. In particular, our proposed two-level dynamic ranking model allows users to adapt the ranking through interaction, thus overcoming the constraints of presenting a one-size-fits-all static ranking. In this model, a user's interactions with the first-level ranking are used to infer this user's intent, so that second-level rankings can be inserted to provide more results relevant to this intent. Unlike previous dynamic ranking models, we provide an algorithm to efficiently compute dynamic rankings with provable approximation guarantees. We also propose the first principled algorithm for learning dynamic ranking functions from training data. In addition to the theoretical results, we provide empirical evidence demonstrating the gains in retrieval quality over conventional approaches.
Year
DOI
Venue
2011
10.1145/2063576.2063623
conference on information and knowledge management
Keywords
DocType
Volume
new dynamic ranking approach,second-level ranking,dynamic ranking function,conventional retrieval system,one-size-fits-all static ranking,conventional approach,two-level dynamic ranking model,previous dynamic ranking model,first-level ranking,dynamic ranking,empirical evidence,structured learning,first principle,information retrieval,algorithms
Conference
abs/1108.2754
Citations 
PageRank 
References 
13
0.75
17
Authors
3
Name
Order
Citations
PageRank
Karthik Raman130923.74
Thorsten Joachims2173871254.06
Pannaga Shivaswamy31275.15