Download
Job-Recommend explores the basics of building a job recommendation workflow, from data preparation to ranking simple candidate matches. It treats job postings and résumés as structured items and applies straightforward matching signals such as keywords, skills overlap, or vectorized features. The repository is educational in spirit, focusing on clarity rather than heavy infrastructure or opaque models. You can study how to transform raw text into features and how to evaluate simple heuristics or baseline models. The code encourages experimentation, inviting you to swap scoring rules, adjust weights, or plug in alternative representations. It serves as a starting point for understanding recommendation pipelines before moving to production-grade systems.
Features
- Baseline scoring of résumés and jobs using transparent signals
- Data preprocessing steps that turn text into usable features
- Example ranking pipeline from candidate generation to ordering
- Hooks for experimenting with weights, thresholds, or embeddings
- Small, readable code suitable for learning and prototyping
- Simple evaluation ideas to compare alternative heuristics
Project Samples
