This is a news recommender system that uses beautiful-soup to scrape news articles, their categories and descriptions to create a data dump. It then uses word embedding techniques such tf-idf, word2vec for content based news recommendation and LightRF, LightFM to explore hybrid and collaborative filtering based recommender models.
active 2024-03-23 → 2024-09-30 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 192 days from 2024-03-23 to 2024-09-30. Pushes: 0 total, peak 0 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 2 total, peak 1 in a day.
- Pushes
- Pull requests
- Issues
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- Stars
Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
Nobody pushed, opened or commented here in the loaded window — this repo's activity is stars and forks only.
Recent activity
Latest issues, pull requests and releases
No issue or PR events — this repo's activity is pushes only.
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 2 stars here means stars gained during the window, not the repo's star count.