Machine learning microservice for library management system project
-
Updated
Nov 13, 2025 - Python
Machine learning microservice for library management system project
Repository hosting code for "Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations" (https://arxiv.org/abs/2402.17152).
A self-assessment tool by @NC3-LU to help business owners implement a better cybersecurity strategy.
Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
Production-ready Redis patterns for e-commerce applications. Includes session management, caching strategies, inventory tracking, recommendations, rate limiting, and analytics - similar to Amazon's architecture.
LinkedIn Recommendation Writer - Generate professional LinkedIn recommendations using GitHub data and AI. This full-stack application analyzes GitHub profiles and creates personalized recommendations powered by Google Gemini AI.
automate the boring stuff!
Moviezinfo Bot is a feature-rich Telegram bot that transforms how users discover and dexplore movies and TV series. Built with Python and integrated with OMDB API, it provides instant access to comprehensive movie information, intelligent recommendations, trailers, and IMDb links. The bot features smart search detection, genre-based recommendations
[KDD 2025] The source code for UQABench
CORE is a plug-and-play conversational agent for any recommender system.
Dartmouth Course Reviews, Rankings, and Recommendations
A dataset containing TLS requirements to assess a webserver's compliance with national cybersecurity agencies
🛍 A real-world e-commerce dataset for session-based recommender systems research.
A unified, comprehensive and efficient recommendation library
This is a repository of public data sources for Recommender Systems (RS).
The code for the paper "MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for Recommendation" (ACM MM'23).
The code for the paper "RAT: Retrieval-Augmented Transformer for Click-Through Rate Prediction" (WWW 24 short paper)
Add a description, image, and links to the recommendations topic page so that developers can more easily learn about it.
To associate your repository with the recommendations topic, visit your repo's landing page and select "manage topics."