Real-time collaborative filtering for item-item similarities
An implementation of the paper Cloud based real-time collaborative filtering for item-item recommendations.
We also stored which items every user rated and hence improved the performance.
The cosine branch has an experimental implementation of similarity calculation using numeric ratings instead of binary data.
You must have redis.py. Install it with this instruction:
$ pip3 install redisScripts are written in Python 3.
Run the following redis servers running before running the scripts:
# cd redis.../src
$ redis-server --port 6379
$ redis-server --port 6380
$ redis-server --port 6381
$ redis-server --port 6382Run the following python scripts via run script:
$ chmod +x run
$ ./runFeed the scripts with data:
$ python3 client.pyMonitor the most similar items to an item live with similars.py script:
$ python3 similars.py 1
Showing the most similar items for 1
[['1220', '0.036'], ['315', '0.036'], ['4886', '0.036'], ['780', '0.036'], ['1923', '0.032'], ['586', '0.032'], ['4973', '0.030'], ['1198', '0.021'], ['457', '0.017'], ['296', '0.014']]
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