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Challenging the Long Tail Recommendation

Summary: Graph-based long-tail recommendation using an undirected edge-weighted user-item graph; analyzes hitting-time for tail-item ranking. Introduces Absorbing Time and entropy-biased Absorbing Cost to improve diversity and accuracy, with empirical gains over state-of-the-art baselines. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10529
Venue
VLDB
Year
2012
Pagerank
5.7584513e-05
Overall Rank
5,015 | 65.12%
DOI
-

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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
2,930 Assessing and Ranking Structural Correlations in Graphs 2011 SIGMOD 7.8723983e-05
5,204 Recsplorer: Recommendation Algorithms Based on Precedence Mining 2010 SIGMOD 5.6308806e-05
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