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Challenging the Long Tail Recommendation

Summary: Introduces graph-based long-tail recommendation using user–item Hitting Time, efficient Absorbing Time, and entropy-biased Absorbing Cost algorithms. These model rating-pair variation to improve sparse-tail accuracy and diversity, outperforming existing recommenders on two real datasets. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
10717
Venue
VLDB
Year
2012
Pagerank
6.7911017e-05
Overall Rank
4,272 | 70.70%
DOI
10.14778/2311906.2311916

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{yin_vldb12,
        title = {{Challenging the Long Tail Recommendation}},
        author = {Yin, Hongzhi and Cui, Bin and Li, Jing and Yao, Junjie and Chen, Chen},
        journal = {PVLDB},
        series = {{VLDB} '12},
        volume = {5},
        number = {9},
        pages = {896--907},
        doi = {10.14778/2311906.2311916},
        url = {https://doi.org/10.14778/2311906.2311916},
        year = {2012}
}

Incoming Citations (Sorted by Pagerank)

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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
3,149 Assessing and Ranking Structural Correlations in Graphs 2011 SIGMOD 7.7066337e-05
6,248 Recsplorer: Recommendation Algorithms Based on Precedence Mining 2010 SIGMOD 5.9425237e-05
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