LibRecommender 0.12.0

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:

• Implemented a number of popular recommendation algorithms such as FM, DIN, LightGCN etc, see full algorithm list.
• A hybrid recommender system, which allows user to use either collaborative-filtering or content-based features. New features can be added on the fly.
• Low memory usage, automatically convert categorical and multi-value categorical features to sparse representation.
• Support training for both explicit and implicit datasets, and negative sampling can be used for implicit dataset.
• Provide end-to-end workflow, i.e. data handling / preprocessing -> model training -> evaluate -> serving.
• Support cold-start prediction and recommendation.
• Provide unified and friendly API for all algorithms. Easy to retrain model with new users/items.

Usage

pure collaborative-filtering example :

import numpy as np
import pandas as pd
from libreco.data import random_split, DatasetPure
from libreco.algorithms import SVDpp  # pure data, algorithm SVD++
from libreco.evaluation import evaluate

data = pd.read_csv("examples/sample_data/sample_movielens_rating.dat", sep="::",
                   names=["user", "item", "label", "time"])

# split whole data into three folds for training, evaluating and testing
train_data, eval_data, test_data = random_split(data, multi_ratios=[0.8, 0.1, 0.1])

train_data, data_info = DatasetPure.build_trainset(train_data)
eval_data = DatasetPure.build_evalset(eval_data)
test_data = DatasetPure.build_testset(test_data)
print(data_info)   # n_users: 5894, n_items: 3253, data sparsity: 0.4172 %

svdpp = SVDpp(task="rating", data_info=data_info, embed_size=16, n_epochs=3, lr=0.001,
              reg=None, batch_size=256)
# monitor metrics on eval_data during training
svdpp.fit(train_data, verbose=2, eval_data=eval_data, metrics=["rmse", "mae", "r2"])

# do final evaluation on test data
print("evaluate_result: ", evaluate(model=svdpp, data=test_data,
                                    metrics=["rmse", "mae"]))
# predict preference of user 2211 to item 110
print("prediction: ", svdpp.predict(user=2211, item=110))
# recommend 7 items for user 2211
print("recommendation: ", svdpp.recommend_user(user=2211, n_rec=7))

# cold-start prediction
print("cold prediction: ", svdpp.predict(user="ccc", item="not item",
                                         cold_start="average"))
# cold-start recommendation
print("cold recommendation: ", svdpp.recommend_user(user="are we good?",
                                                    n_rec=7,
                                                    cold_start="popular"))

include features example :

import numpy as np
import pandas as pd
from libreco.data import split_by_ratio_chrono, DatasetFeat
from libreco.algorithms import YouTubeRanking  # feat data, algorithm YouTubeRanking

data = pd.read_csv("examples/sample_data/sample_movielens_merged.csv", sep=",", header=0)
data["label"] = 1  # convert to implicit data and do negative sampling afterwards

# split into train and test data based on time
train_data, test_data = split_by_ratio_chrono(data, test_size=0.2)

# specify complete columns information
sparse_col = ["sex", "occupation", "genre1", "genre2", "genre3"]
dense_col = ["age"]
user_col = ["sex", "age", "occupation"]
item_col = ["genre1", "genre2", "genre3"]

train_data, data_info = DatasetFeat.build_trainset(
    train_data, user_col, item_col, sparse_col, dense_col
)
test_data = DatasetFeat.build_testset(test_data)
train_data.build_negative_samples(data_info)  # sample negative items for each record
test_data.build_negative_samples(data_info)
print(data_info)  # n_users: 5962, n_items: 3226, data sparsity: 0.4185 %

ytb_ranking = YouTubeRanking(task="ranking", data_info=data_info, embed_size=16,
                             n_epochs=3, lr=1e-4, batch_size=512, use_bn=True,
                             hidden_units="128,64,32")
ytb_ranking.fit(train_data, verbose=2, shuffle=True, eval_data=test_data,
                metrics=["loss", "roc_auc", "precision", "recall", "map", "ndcg"])

# predict preference of user 2211 to item 110
print("prediction: ", ytb_ranking.predict(user=2211, item=110))
# recommend 7 items for user 2211
print("recommendation(id, probability): ", ytb_ranking.recommend_user(user=2211, n_rec=7))

# cold-start prediction
print("cold prediction: ", ytb_ranking.predict(user="ccc", item="not item",
                                               cold_start="average"))
# cold-start recommendation
print("cold recommendation: ", ytb_ranking.recommend_user(user="are we good?",
                                                          n_rec=7,
                                                          cold_start="popular"))

For more examples and usages, see User Guide

Data Format

JUST normal data format, each line represents a sample. One thing is important, the model assumes that user, item, and label column index are 0, 1, and 2, respectively. You may wish to change the column order if that's not the case. Take for Example, the movielens-1m dataset:

1::1193::5::978300760
1::661::3::978302109
1::914::3::978301968
1::3408::4::978300275

Besides, if you want to use some other meta features (e.g., age, sex, category etc.), you need to tell the model which columns are [sparse_col, dense_col, user_col, item_col], which means all features must be in a same table. See above YouTubeRanking for example.

Also note that your data should not contain missing values.

Serving

For how to serve a trained model in LibRecommender, see Serving Guide .

Installation & Dependencies

From pypi :  

$ pip install LibRecommender

To build from source, you 'll first need Cython and Numpy:

$ # pip install numpy cython
$ git clone https://github.com/massquantity/LibRecommender.git
$ cd LibRecommender
$ python setup.py install

Basic Dependencies for libreco:

• Python >= 3.6
• TensorFlow >= 1.15
• PyTorch >= 1.10
• Numpy >= 1.19.5
• Cython >= 0.29.0
• Pandas >= 1.0.0
• Scipy >= 1.2.1
• scikit-learn >= 0.20.0
• gensim >= 4.0.0
• tqdm
• nmslib (optional)

LibRecommender is tested under TensorFlow 1.15, 2.5, 2.8 and 2.10. If you encounter any problem during running, feel free to open an issue. If you are using Python 3.6, you also need to install dataclasses, since it is first included in Python 3.7.

Known issue: Sometimes one may encounter errors like ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 88 from C header, got 80 from PyObject. In this case try upgrading numpy, and version 1.22.0 or higher is probably a safe option.

The table below shows some compatible version combinations:

Python	Numpy	TensorFlow	OS
3.6	1.19.5	1.15, 2.5	linux, windows, macos
3.7	1.20.3, 1.21.6	1.15, 2.5, 2.8, 2.10	linux, windows, macos
3.8	1.22.4, 1.23.2	2.5, 2.8, 2.10	linux, windows, macos
3.9	1.22.4, 1.23.2	2.5, 2.8, 2.10	linux, windows, macos
3.10	1.22.4, 1.23.2	2.8, 2.10	linux, windows, macos

Optional Dependencies for libserving:

• sanic >= 22.3
• requests
• aiohttp
• pydantic
• ujson
• redis
• redis-py >= 4.2.0
• faiss >= 1.5.2
• TensorFlow Serving == 2.8.2

Docker

One can also use the library in a docker container without installing dependencies, see Docker.

References

Algorithm	Category1	Backend	Sequence2	Graph3	Embedding4	Paper
userCF / itemCF	pure	Cython				Item-Based Collaborative Filtering Recommendation Algorithms
SVD	pure	TensorFlow1			:heavy_check_mark:	Matrix Factorization Techniques for Recommender Systems
SVD++	pure	TensorFlow1			:heavy_check_mark:	Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model
ALS	pure	Cython			:heavy_check_mark:	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	TensorFlow1				Neural Collaborative Filtering
BPR	pure	Cython, TensorFlow1			:heavy_check_mark:	BPR: Bayesian Personalized Ranking from Implicit Feedback
Wide & Deep	feat	TensorFlow1				Wide & Deep Learning for Recommender Systems
FM	feat	TensorFlow1				Factorization Machines
DeepFM	feat	TensorFlow1				DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
YouTubeRetrieval	feat	TensorFlow1	:heavy_check_mark:		:heavy_check_mark:	Deep Neural Networks for YouTube Recommendations
YouTubeRanking	feat	TensorFlow1	:heavy_check_mark:			Deep Neural Networks for YouTube Recommendations
AutoInt	feat	TensorFlow1				AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
DIN	feat	TensorFlow1	:heavy_check_mark:			Deep Interest Network for Click-Through Rate Prediction
Item2Vec	pure	/	:heavy_check_mark:		:heavy_check_mark:	Item2Vec: Neural Item Embedding for Collaborative Filtering
RNN4Rec / GRU4Rec	pure	TensorFlow1	:heavy_check_mark:		:heavy_check_mark:	Session-based Recommendations with Recurrent Neural Networks
Caser	pure	TensorFlow1	:heavy_check_mark:		:heavy_check_mark:	Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
WaveNet	pure	TensorFlow1	:heavy_check_mark:		:heavy_check_mark:	WaveNet: A Generative Model for Raw Audio
DeepWalk	pure	/		:heavy_check_mark:	:heavy_check_mark:	DeepWalk: Online Learning of Social Representations
NGCF	pure	PyTorch		:heavy_check_mark:	:heavy_check_mark:	Neural Graph Collaborative Filtering
LightGCN	pure	PyTorch		:heavy_check_mark:	:heavy_check_mark:	LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
GraphSage	feat	DGL, PyTorch		:heavy_check_mark:	:heavy_check_mark:	Inductive Representation Learning on Large Graphs
PinSage	feat	DGL, PyTorch		:heavy_check_mark:	:heavy_check_mark:	Graph Convolutional Neural Networks for Web-Scale Recommender Systems

^{[1] Category: pure means collaborative-filtering algorithms which only use behavior data, feat means other side-features can be included. ↩}

^{[2] Sequence: Algorithms that leverage user behavior sequence. ↩}

^{[3] Graph: Algorithms that leverage graph information, including Graph Embedding (GE) and Graph Neural Network (GNN) . ↩}

^{[4] Embedding: Algorithms that can generate final user and item embeddings. ↩}

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