Efficient network aware search in collaborative tagging sites

The popularity of collaborative tagging sites presents a unique opportunity to explore keyword search in a context where query results are determined by the opinion of a network of taggers related to a seeker. In this paper, we present the first in-depth study of network-aware search. We investigate efficient top-k processing when the score of an answer is computed as its popularity among members of a seeker's network. We argue that obvious adaptations of top-k algorithms are too space-intensive, due to the dependence of scores on the seeker's network. We therefore develop algorithms based on maintaining score upper-bounds. The global upper-bound approach maintains a single score upper-bound for every pair of item and tag, over the entire collection of users. The resulting bounds are very coarse. We thus investigate clustering seekers based on similar behavior of their networks. We show that finding the optimal clustering of seekers is intractable, but we provide heuristic m...

• Type: Journal
• Year: 2008
• Where: PVLDB
• Authors: Sihem Amer-Yahia, Michael Benedikt, Laks V. S. Lakshmanan, Julia Stoyanovich
• Keywords: Collaborative Tagging Sites, Efficient Top-k Processing, Global Upper-bound Approach, PVLDB 2008