Recommended System Play-Ground.

This is the place I try to figure out how to implement recommendation algos

data sets

content based demo code
nearest neighbor (User-Based CF, Item-Based CF)
- issue: very inefficient to compute pairwise similarity(distance)
- demo code
matrix factorization (explicit/implicit ALS, SGD ...) demo
Learning to Rank
- LightFM coldstart

Approximate Nearest Neighbors
- issue: can't install properyly under windows env (solved)
  - annoy -- ubuntu16.04, windows server2012 (update conda to python 3.6 version)
  - nmslib -- failed
- how to use it? lightFM+annoy

Name		Name	Last commit message	Last commit date
Latest commit History 97 Commits
fm		fm
funds-dataset		funds-dataset
funds_web		funds_web
lightfm-paper		lightfm-paper
ml-dataset-100k		ml-dataset-100k
notebook		notebook
paper		paper
rec-a-sketch		rec-a-sketch
rec_funds		rec_funds
sql		sql
temp		temp
.gitignore		.gitignore
01_explict_CF_intro.ipynb		01_explict_CF_intro.ipynb
01_lightfm_movieLens.py		01_lightfm_movieLens.py
02_explicit_ALS_SGD.ipynb		02_explicit_ALS_SGD.ipynb
02_lightfm_lr.py		02_lightfm_lr.py
03_implict_ALS_sketchlab.ipynb		03_implict_ALS_sketchlab.ipynb
03_lightfm_coldstart.py		03_lightfm_coldstart.py
04_lightFM.ipynb		04_lightFM.ipynb
04_lightFM_rank.py		04_lightFM_rank.py
05_funds_test.py		05_funds_test.py
06_knn_funds.py		06_knn_funds.py
07_quickTestLightFM_funds.py		07_quickTestLightFM_funds.py
08_lightfm_annoy_funds.ipynb		08_lightfm_annoy_funds.ipynb
KNNmodel.py		KNNmodel.py
LightFM_ANN_example.py		LightFM_ANN_example.py
README.md		README.md
__init__.py		__init__.py
annoy_funds_test.ipynb		annoy_funds_test.ipynb
cb_funds.ipynb		cb_funds.ipynb
explict_CF.py		explict_CF.py
helpers.py		helpers.py
knn_funds_recommendation.py		knn_funds_recommendation.py
lastfm_calculate_similar.py		lastfm_calculate_similar.py
lastfm_implicit.ipynb		lastfm_implicit.ipynb
lastfm_implicit.py		lastfm_implicit.py
lastfm_sim.ipynb		lastfm_sim.ipynb
mem_test.py		mem_test.py
rec_helper.py		rec_helper.py