vbpr_tradesy.py

# Copyright 2018 The Cornac Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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# ============================================================================
"""
Example for Visual Bayesian Personalized Ranking (VBPR)
Original data: http://jmcauley.ucsd.edu/data/tradesy/
"""

import cornac
from cornac.datasets import tradesy
from cornac.data import ImageModality
from cornac.eval_methods import RatioSplit

# VBPR extends Bayesian Personalized Randing to leverage item visual features (extracted from product images using CNN)
# The necessary data can be loaded as follows
feedback = tradesy.load_feedback()
features, item_ids = tradesy.load_feature()  # BIG file

# Instantiate a ImageModality, it make it convenient to work with visual auxiliary information
# For more details, please refer to the tutorial on how to work with auxiliary data
item_image_modality = ImageModality(features=features, ids=item_ids, normalized=True)

# Define an evaluation method to split feedback into train and test sets
ratio_split = RatioSplit(data=feedback,
                         test_size=0.1, rating_threshold=0.5,
                         exclude_unknowns=True, verbose=True,
                         item_image=item_image_modality)

# Instantiate CVAE
vbpr = cornac.models.VBPR(k=10, k2=20, n_epochs=50, batch_size=100, learning_rate=0.005,
                          lambda_w=1, lambda_b=0.01, lambda_e=0.0, use_gpu=True)

# Instantiate evaluation measures
auc = cornac.metrics.AUC()
rec_50 = cornac.metrics.Recall(k=50)

# Put everything together into an experiment and run it
exp = cornac.Experiment(eval_method=ratio_split,
                        models=[vbpr],
                        metrics=[auc, rec_50])
exp.run()