pySmore

• WARNING: currently it runs in 40% the speed of Original C++ version, but provides better experience of compatibility with Python scripts.
• Original C++ version: SMORe

Feature checklist:

• MF (Matrix Factorization)
• BPR (Bayesian Personalized Ranking)
  • BPR: Bayesian personalized ranking from implicit feedback
• LINE(Large-scale Information Network Embedding)
  • LINE: Large-scale Information Network Embedding
• DeepWalk
  • DeepWalk: online learning of social representations
• Walklets
  • Don't Walk, Skip! Online Learning of Multi-scale Network Embeddings
• HPE (Heterogeneous Preference Embedding)
  • Query-based Music Recommendations via Preference Embedding
• APP (Asymmetric Proximity Preserving graph embedding)
  • Scalable Graph Embedding for Asymmetric Proximity
• WARP-like
  • WSABIE: Scaling Up To Large Vocabulary Image Annotation
  • Learning to Rank Recommendations with the k-Order Statistic Loss
• HOP-REC
  • HOP-Rec: High-Order Proximity for Implicit Recommendation
• CSE (named nemf & nerank in cli)
  • Collaborative Similarity Embedding for Recommender Systems

Run Example

cd pySmore
python3 example.py

Get started

import pysmore.models.mf as MF
import pysmore.models.bpr as BPR
import pysmore.models.line as LINE

# Choose a graph embedding method
trainer = BPR  # or MF, LINE

# Create a graph with given user-item interaction data
trainer.create_graph("data/ui.train.txt", embedding_dimension=6)

# Pass the parameters that you'd like for training!
trainer.set_param({
    'init_lr':  0.025,  # initial learning rate
    'l2_reg':   0.01    # L2-Regularize ratio
})

# Start training!
# Noted that `update_times` will be multiplied by 1 million
# `workers` is the amount of process to use, NOT THREADS
trainer.train(update_times=1e-4, workers=4)

# Afterwards, output the embeddings.
trainer.save_embeddings(file_prefix="bpr")

Task

Given a network input:

userA	itemA	3
userA	itemC	5
userB	itemA	1
userB	itemB	5
userC	itemA	4

The model learns the representations of each vertex:

userA	-0.068195	0.105852	0.056242	0.084970	-0.209601	-0.018169
itemA	-0.033628	0.046754	0.030732	0.035540	-0.105440	-0.008107
itemC	-0.114769	0.181540	0.092762	0.146976	-0.351410	-0.031107
userB	-0.050903	0.013206	0.077547	-0.013286	-0.179966	-0.003265
itemB	-0.088020	0.015789	0.137878	-0.031181	-0.313660	-0.004543
userC	-0.060036	0.086218	0.053508	0.066621	-0.187579	-0.014900

We can calculate the results (dot product):

userA	itemA	0.034238
userA	itemC	0.118970
userB	itemA	0.023242
userB	itemB	0.072258
userC	itemA	0.029961