Improved Search for Socially Annotated Data
Summary: Uses exclusively user-generated tag sequences to perform search and resource ranking. Proposes probabilistic interpolated n-gram models over tag sequences and a scalable constrained optimization framework for training and incremental maintenance, validated on del.icio.us-scale data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Nikos Sarkas
- 2. Gautam Das
- 3. Nick Koudas
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,900 | Partitioning and Ranking Tagged Data Sources | 2013 | VLDB | 5.281001e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7 | Optimal Aggregation Algorithms for Middleware [Extended Abstract] | 2001 | PODS | 0.0015496097 |
| 2,002 | Efficient Network Aware Search in Collaborative Tagging Sites | 2008 | VLDB | 9.818583e-05 |
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