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)
Incoming Non-self Citations Over Time
Authors
- 1. Bhargav Kanagal
- 2. Amr Ahmed
- 3. Sandeep Pandey
- 4. Vanja Josifovski
- 5. Jeff Yuan
- 6. Lluis Garcia-Pueyo
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,466 | RecDB in Action: Recommendation Made Easy in Relational Databases | 2013 | VLDB | 6.2757272e-05 |
| 11,961 | Inferring Continuous Dynamic Social Influence and Personal Preference for Temporal Behavior Prediction | 2015 | VLDB | 5.1725247e-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 |
|---|---|---|---|---|
| 449 | Mining Generalized Association Rules | 1995 | VLDB | 0.0001834306 |
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