librec 0.0.5

A collaborative-filtering and content-based recommender system for both explicit and implicit datasets.

LibRecommender

Overview

LibRecommender is an easy-to-use recommender system focused on end-to-end recommendation. The main features are:

• Implement a number of popular recommendation algorithms such as SVD, DeepFM, BPR etc.

• Allow user to use pure behavior features as well as other meta features.

• Automatically convert categorical features to sparse representation, thus ease the memory usage.

• Enable negative sampling for implicit dataset.

• Using Cython or Tensorflow to accelerate model training.

• Provide end-to-end workflow, i.e. data handling / preprocessing -> model training -> evaluate -> serving.

Usage

from libreco.dataset import DatasetFeat
from libreco.algorithms import DeepFMFeat

conf = {
    "data_path": "path/to/your/data",
    "length": 500000,
    "user_col": 0,
    "item_col": 1,
    "label_col": 2,
    "numerical_col": [4],
    "categorical_col": [3, 5, 6, 7, 8],
    "merged_categorical_col": None,
    "user_feature_cols": [3, 4, 5],
    "item_feature_cols": [6, 7, 8],
    "convert_implicit": True,
    "build_negative": True,
    "num_neg": 2,
#    "batch_size": 2048,
    "sep": ",",
}

dataset = DatasetFeat(include_features=True)
dataset.build_dataset(**conf)

dfm = DeepFMFeat(lr=0.0002, n_epochs=10000, reg=0.1, embed_size=50,
                    batch_size=2048, dropout=0.0, task="ranking", neg_sampling=True)
dfm.fit(dataset, pre_sampling=False, verbose=1)
print(dfm.predict(1959, 1992))
print(dfm.recommend_user(19500, 7))

Data Format

Installation & Dependencies

• Python 3.5 +
• tensorflow >= 1.12
• numpy >= 1.13
• pandas >= 0.21.0
• scipy >= 0.19.1
• scikit-learn >= 0.20.1

References

Algorithm	Category	Paper
userKNN / itemKNN	pure	Item-Based Collaborative Filtering Recommendation Algorithms
SVD	pure	Matrix Factorization Techniques for Recommender Systems
SVD ++	pure	Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model
superSVD	pure	Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model
ALS	pure	1. Matrix Completion via Alternating Least Square(ALS) / 2. Collaborative Filtering for Implicit Feedback Datasets / 3. Applications of the Conjugate Gradient Method for Implicit Feedback Collaborative Filtering
NCF	pure	Neural Collaborative Filtering
BPR	pure	BPR: Bayesian Personalized Ranking from Implicit Feedback
Wide & Deep	feat	Wide & Deep Learning for Recommender Systems
FM	feat	Factorization Machines
DeepFM	feat	DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
Youtube-Recommendation	feat	Deep Neural Networks for YouTube Recommendations