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Enriching Recommendation Models with Logic Conditions

Summary: RecLogic augments ML-based recommenders with graph-based TIE rules that embed ML predicates to reduce misclassifications without retraining. It learns TIEs iteratively, enabling a PTIME parallel recommendation algorithm with 22.89% gains (up to 33.10%) on real data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6713
Venue
SIGMOD
Year
2023
Pagerank
4.1945683e-05
Overall Rank
11,209 | 22.03%
DOI
10.1145/3617330

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Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
9,400 Explaining GNN-based Recommendations in Logic 2025 VLDB 4.3441378e-05
9,434 Rock: Cleaning Data by Embedding ML in Logic Rules 2024 SIGMOD 4.3430376e-05
10,029 Outliers: The Good, the Bad and the Ugly 2026 SIGMOD 4.1945683e-05
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