Recommender Systems
Recommender systems are useful for recommending users items based on their past preferences. Broadly, recommender systems can be split into content-based and collaborative-filtering types. Such recommender systems are predictive in nature. For example, we predict what the user might want to buy next. Here, we use the Surprise python package. http://surpriselib.com/
Example: Amazon - "You May Also Like"
Association Mining
Also, we will delve a bit into association mining, which is a form of descriptive analytics. This can be used to develop marketing strategies like cross-selling or up-selling. Here, we use the mlxtend python package. https://github.com/rasbt/mlxtend
Example: Amazon - "Frequently Bought Together"
Nearest Neighbours of Item - nearest_neighbours.py
This code demonstrates how to get the 10 most related items (nearest neighbours) to the movie item "Clockwork Orange".
User and Item based collaborative filtering - collaborative_filtering_exploration.py
Algorithms used: http://surprise.readthedocs.io/en/stable/knn_inspired.html
This code demonstrates how collaborative filtering for user-based and item-based methods can be done in Python.
Advantages of item-based filtering over user-based filtering :
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Scales Better : User-based filtering does not scale well as user likes/interests may change frequently. Hence, the recommendation needs to be re-trained frequently.
-
Computationally Cheaper : In many cases, there are way more users than items. It makes sense to use item-based filtering in this case.
A famous example of item-based filtering is Amazon's recommendation engine.
https://www.cs.umd.edu/~samir/498/Amazon-Recommendations.pdf
Grid Search on KNNMeans Algorithm - grid_search.py
An extensive grid search to find the best hyper-parameters for KNNMeans on this dataset. Warning: Takes a long time!
Results:
Grid Search Results:
0.931737661568928
{'k': 40, 'bsl_options': {'n_epochs': 1, 'reg_i': 40, 'method': 'als', 'reg_u': 25}, 'sim_options': {'user_based': False, 'name': 'pearson_baseline'}}
Evaluating RMSE, MAE of algorithm KNNWithMeans on 5 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Mean Std
MAE (testset) 0.7161 0.7176 0.7171 0.7178 0.7227 0.7182 0.0023
RMSE (testset) 0.9131 0.9185 0.9173 0.9171 0.9239 0.9180 0.0035
Fit time 1.11 1.12 1.10 1.05 0.96 1.07 0.06
Test time 2.06 2.06 2.05 2.05 2.05 2.05 0.00
{'fit_time': (1.1072561740875244, 1.1171300411224365, 1.1014506816864014, 1.052947998046875, 0.9627974033355713), 'test_rmse': array([0.91305966, 0.91854328, 0.91732058, 0.9170613 , 0.92394471]), 'test_mae': array([0.71609261, 0.71756385, 0.7170833 , 0.71775924, 0.72269525]), 'test_time': (2.0553717613220215, 2.055539846420288, 2.047955274581909, 2.052337884902954, 2.045203685760498)}
Singular Vector Decomposition (SVD, SVD++) - SVD.py
These algorithms are famous for being the baseline algos for winning the Netlix Prize.
http://buzzard.ups.edu/courses/2014spring/420projects/math420-UPS-spring-2014-gower-netflix-SVD.pdf
For SVD
Evaluating RMSE, MAE of algorithm KNNWithMeans on 5 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Mean Std
MAE (testset) 0.7191 0.7158 0.7138 0.7166 0.7254 0.7181 0.0040
RMSE (testset) 0.9173 0.9162 0.9125 0.9179 0.9271 0.9182 0.0048
Fit time 1.04 1.11 1.03 1.01 0.90 1.02 0.07
Test time 2.08 2.06 2.06 2.05 2.06 2.06 0.01
{'test_mae': array([0.71909501, 0.71579784, 0.71384567, 0.71656901, 0.72541331]), 'fit_time': (1.0419056415557861, 1.1055827140808105, 1.0349535942077637, 1.01346755027771, 0.9016950130462646), 'test_rmse': array([0.91726677, 0.91622589, 0.91245293, 0.91793969, 0.92711428]), 'test_time': (2.0783369541168213, 2.0616250038146973, 2.0627968311309814, 2.049354314804077, 2.058506727218628)}
SVD : Test Set
RMSE: 0.9000
For SVD++ (Before running : Be warned it is much slower than SVD)
Grid Search Results:
0.9167768433405562
{'n_factors': 40, 'reg_all': 0.1, 'n_epochs': 40, 'lr_all': 0.008}
Evaluating RMSE, MAE of algorithm SVDpp on 5 split(s).
Fold 1 Fold 2 Fold 3 Fold 4 Fold 5 Mean Std
RMSE (testset) 0.9104 0.9053 0.9111 0.9133 0.9124 0.9105 0.0028
MAE (testset) 0.7188 0.7145 0.7204 0.7224 0.7203 0.7193 0.0027
Fit time 336.05 334.75 335.30 334.70 339.24 336.01 1.69
Test time 1.79 1.84 1.86 1.84 1.74 1.81 0.04
SVD++ : Test Set
RMSE: 0.9141
A little slow? Can we use GPU to accelerate this? Yes! (for future explorations)
https://devblogs.nvidia.com/numba-python-cuda-acceleration/
http://www.nvidia.com/content/GTC/documents/1034_GTC09.pdf
https://github.com/NVIDIA/DeepRecommender
C-Based Implementations
libFM - Recommended by many https://github.com/srendle/libfm
xLearn - supports out-of-core training, fast, has Python and R APIs https://github.com/aksnzhy/xlearn
Association Mining : association_mining.py
Demonstrates how to get frequent items and association rules using the mlxtend package.
One finding is this:
Jumbo Storage Bag Suki is usually bought together with Jumbo Bag Red Retrospot.
Association Mining from another repo : https://github.com/lppier/League_Of_Legends_Data_Analytics/blob/master/AssociationAnalysis.Rmd
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