Database Paper Browser

Back to papers

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
10535
Venue
VLDB
Year
2012
Pagerank
6.3173499e-05
Overall Rank
5,365 | 62.72%
DOI
-

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Previous Page 1 / 1 Next

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
449 Mining Generalized Association Rules 1995 VLDB 0.0001834306
Previous Page 1 / 1 Next

Semantically Similar Papers