Papers about recommendation systems that I am interested in
TEM: Tree-enhanced Embedding Model for Explainable Recommendation, WWW2018
TETUP: Code for "Towards Explainable Temporal User Profiling with LLMs" (ExUM 2025). This project proposes a content-based recommendation framework that generates short-term and long-term user profiles using LLMs, enabling interpretable and personalized recommendations with temporal awareness.
A local-first anime discovery engine with explainable rankings, mood discovery, head-to-head comparisons, catalog exploration, and resilient artwork enrichment. Your next anime, ranked with reasons.
Personal project. Privacy-first, self-hosted Calibre and KOReader dashboard with cross-device reading progress, stats and Wrapped, local share cards, and explainable recommendations. Shelf-diversity views are built from sourced book tags and never infer or label an author's identity.
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