Fast dynamic reranking in large graphs

In this paper we consider the problem of re-ranking search results by incorporating user feedback. We present a graph theoretic measure for discriminating irrelevant results from relevant results using a few labeled examples provided by the user. The key intuition is that nodes relatively closer (in graph topology) to the relevant nodes than the irrelevant nodes are more likely to be relevant. We present a simple sampling algorithm to evaluate this measure at specific nodes of interest, and an efficient branch and bound algorithm to compute the top k nodes from the entire graph under this measure. On quantifiable prediction tasks the introduced measure outperforms other diffusion-based proximity measures which take only the positive relevance feedback into account. On the Entity-Relation graph built from the authors and papers of the entire DBLP citation corpus

• Type: Conference
• Year: 2009
• Where: WWW
• Authors: Purnamrita Sarkar, Andrew W. Moore
• Keywords: Graph Theoretic Measure, Internet Technology, Irrelevant Nodes, Specific Nodes, WWW 2009