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Supercharging Recommender Systems using Taxonomies for Learning User Purchase Behavior

Summary: Proposes taxonomy-based latent factor model (TF): fuses taxonomies with factors to tackle sparsity and cold-start. Scalable training and taxonomy-guided inference via parallel cores; order Markov chains for temporal dynamics, delivering faster, more accurate recommendations. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10722
Venue
VLDB
Year
2012
Pagerank
6.2210009e-05
Overall Rank
5,433 | 62.73%
DOI
10.14778/2336664.2336667

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{kanagal_vldb12,
        title = {{Supercharging Recommender Systems using Taxonomies for Learning User Purchase Behavior}},
        author = {Kanagal, Bhargav and Ahmed, Amr and Pandey, Sandeep and Josifovski, Vanja and Yuan, Jeff and Garcia-Pueyo, Lluis},
        journal = {PVLDB},
        series = {{VLDB} '12},
        volume = {5},
        number = {10},
        pages = {956--967},
        doi = {10.14778/2336664.2336667},
        url = {https://doi.org/10.14778/2336664.2336667},
        year = {2012}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
5,544 RecDB in Action: Recommendation Made Easy in Relational Databases 2013 VLDB 6.180013e-05
12,151 Inferring Continuous Dynamic Social Influence and Personal Preference for Temporal Behavior Prediction 2015 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
460 Mining Generalized Association Rules 1995 VLDB 0.00018071773
Previous Page 1 / 1 Next

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