Book recommendation app with a hybrid model, neural ranking experiments, and documented evaluation.
-
Updated
Oct 29, 2026 - Jupyter Notebook
Book recommendation app with a hybrid model, neural ranking experiments, and documented evaluation.
Interactive Streamlit book recommendation app using item-based collaborative filtering and scikit-learn nearest neighbors. Includes Book-Crossing data, training notebook, saved models, and a detailed README with an app screenshot.
按个人口味推荐中文网络小说的 AI Skill,含舆论核查与可信度标注
微信读书体验卡可读书目清单|抓分类页与榜单 → 神作榜 / 神作潜力榜 / 好评如潮榜|体验卡免费能读的高分书单,合并同书多版本,出单文件 HTML 报告 + 6 sheet Excel
微信读书书架价值评分与零钱购书方案|四层评分 + 四套购书方案 + 余额 ≤2 元零钱模式|16-17 sheet 报告 + 单文件 HTML
AI-powered book recommendation system using Google Books API
A machine learning system that recommends books based on user ratings using K-Nearest Neighbors (KNN).
Intelligent library assistant powered by First-Order Logic Knowledge Base (10,538 books), Forward/Backward Chaining rule inference, and Random-Restart Hill Climbing optimization.
A full-stack BookStore mobile application built with React Native, TypeScript, REST API, and JWT authentication.
Flask + MongoDB book recommendation app powered by an Apache Airflow Apriori pipeline
📚 AI-Powered English Book Recommender — Adaptive Bayesian vocabulary estimation, matching books with 80%+ coverage for Chinese learners
可自行部署的网络小说发现工具,聚合多平台榜单,支持按频道、题材和阅读偏好筛选推荐。
A bilingual literary discovery product that starts from a familiar writer and opens three deliberately different reading paths.
📖✨ 독서 감상을 AI가 분석해 비슷한 분위기의 책을 추천해주는 모바일 앱 — pgvector 임베딩 × 카카오 도서 검색 하이브리드 🤖📚
Open-source FastAPI recommendation engine for books • backend architecture • data handling • scalable systems
AI-powered book discovery and chat application built with ASP.NET Core 8 and Google Gemini API. Features include AI chat for book recommendations, Google Books search, user authentication, and chat history.
AI-powered bookshelf scanner - photograph a shelf, get instant book detection via Gemini vision, Google Books metadata, and OpenAI-personalized recommendations. React 19 + Express 5 + PostgreSQL, deployed on Vercel.
基于语义向量检索、情感分析和 LLM 推荐语生成的智能图书推荐系统,支持类别与情绪筛选并通过 Gradio 展示结果。
To associate your repository with the book-recommendation topic, visit your repo's landing page and select "manage topics."