https://github.com/loggerhead/kdd_2012_track1
A simple solution of 2012 KDD Cup Track 1
https://github.com/loggerhead/kdd_2012_track1
Last synced: 3 months ago
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A simple solution of 2012 KDD Cup Track 1
- Host: GitHub
- URL: https://github.com/loggerhead/kdd_2012_track1
- Owner: loggerhead
- License: wtfpl
- Created: 2015-12-15T08:39:52.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2017-03-17T03:35:08.000Z (over 8 years ago)
- Last Synced: 2025-03-27T13:45:26.691Z (3 months ago)
- Language: Python
- Size: 15.6 KB
- Stars: 4
- Watchers: 2
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
This is a simple solution of [2012 KDD Cup Track 1](http://www.kddcup2012.org/c/kddcup2012-track1), which implemented Latent Factor Model by using Stochastic Gradient Descent algorithm, and most idea is came from `2.2` and `3.1` sections of paper [Context-aware Ensemble of Multifaceted Factorization Models for Recommendation Prediction in Social Networks](https://kaggle2.blob.core.windows.net/competitions/kddcup2012/2748/media/Shanda3.pdf).
# Run
For saving your time, I strongly recommend you to install `PyPy` which is roughly three times faster than `CPython` in my test.
1. Change `config.py` file to tell the program where to find the datasets.
2. `./run.sh`
3. Press `Ctrl-C` in terminal whenever you want to end the training loop.
# Dataset
There are four datasets needed for running:
* [rec_log_train](https://coding.net/u/loggerhead/p/KDD_2012_Track1/git/raw/master/data/rec_log_train.csv.lrz)
* [rec_log_test](https://coding.net/u/loggerhead/p/KDD_2012_Track1/git/raw/master/data/rec_log_test.csv.lrz)
* [KDD_Track1_solution](https://coding.net/u/loggerhead/p/KDD_2012_Track1/git/raw/master/data/KDD_Track1_solution.csv)
* [user_profile](https://coding.net/u/loggerhead/p/KDD_2012_Track1/git/raw/master/data/user_profile.csv.lrz)
I have made some little changes to the orignal datasets:
* remove header from each file
* replace separator from `\t` (tab) to `,` (comma)
If you download datasets from above links, you will found some `.lrz` files and you need use [lrzip](https://github.com/ckolivas/lrzip) to uncompress.
```bash
# install `lrzip`
apt-get install lrzip
# if you are OSX user, run below command to install `lrzip`
# brew install lrzip
lrzip -d *.lrz
```
# Running log
```
Getting summary of training dataset...
======================== Summary of 'rec_log_train.csv' ========================
Users: 1392873 Items: 4710 Users/Items: 295.73
+1: 5253828 -1: 67955449 +1/-1: 0.08
Begin time: 1318348785 End time:1321027199 Interval: 2678414s = 744.00 h = 31.00 d
================== Distribution of user active time (in hour) ==================
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Getting summary of user profile...
============================= Distribution of age ==============================
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============================ Distribution of gender ============================
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============================ Distribution of tweet =============================
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============================= Distribution of tags =============================
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Preprocessing...
Training...
init LFM... 26.158s
408th trainning used 21.1ss |e[u][i]| = 0.251114^C
Exit program after finish current work!
409th trainning used 22.6s |e[u][i]| = 0.251115
predict and write result... 500.564s
Converting predicted result to submission format...
convert predict result to dict... 155.102s
convert to submission format... 50.497s
Computing mAP@3...
Public rank: 412 mAP@3: 0.31774
Private rank: 422 mAP@3: 0.30857
```