Add an example for VAECF with CiteULike dataset

README.md

@@ -140,7 +140,7 @@ The recommender models supported by Cornac are listed below. Why don't you join
 | 2018 | [Collaborative Context Poisson Factorization (C2PF)](cornac/models/c2pf), [paper](https://www.ijcai.org/proceedings/2018/0370.pdf) | N/A | [c2pf_exp.py](examples/c2pf_example.py)
 |      | [Multi-Task Explainable Recommendation (MTER)](cornac/models/mter), [paper](https://arxiv.org/pdf/1806.03568.pdf) | [requirements.txt](cornac/models/mter/requirements.txt) | [mter_exp.py](examples/mter_example.py)
 |      | [Probabilistic Collaborative Representation Learning (PCRL)](cornac/models/pcrl), [paper](http://www.hadylauw.com/publications/uai18.pdf) | [requirements.txt](cornac/models/pcrl/requirements.txt) | [pcrl_exp.py](examples/pcrl_example.py)
-|      | [Variational Autoencoder for Collaborative Filtering (VAECF)](cornac/models/vaecf), [paper](https://arxiv.org/pdf/1802.05814.pdf) | [requirements.txt](cornac/models/vaecf/requirements.txt) |
+|      | [Variational Autoencoder for Collaborative Filtering (VAECF)](cornac/models/vaecf), [paper](https://arxiv.org/pdf/1802.05814.pdf) | [requirements.txt](cornac/models/vaecf/requirements.txt) | [vaecf_example.py](examples/vaecf_example.py)
 | 2017 | [Collaborative Variational Autoencoder (CVAE)](cornac/models/cvae), [paper](http://eelxpeng.github.io/assets/paper/Collaborative_Variational_Autoencoder.pdf) | [requirements.txt](cornac/models/cvae/requirements.txt) | [cvae_exp.py](examples/cvae_example.py)
 |      | [Generalized Matrix Factorization (GMF)](cornac/models/ncf), [paper](https://arxiv.org/pdf/1708.05031.pdf) | [requirements.txt](cornac/models/ncf/requirements.txt) | [ncf_exp.py](examples/ncf_example.py)
 |      | [Indexable Bayesian Personalized Ranking (IBPR)](cornac/models/ibpr), [paper](http://www.hadylauw.com/publications/cikm17a.pdf) | [requirements.txt](cornac/models/ibpr/requirements.txt) | [ibpr_exp.py](examples/ibpr_example.py)

examples/README.md

@@ -56,6 +56,8 @@
 
 [sorec_filmtrust.py](sorec_filmtrust.py) - Social Recommendation using PMF (Sorec) with FilmTrust dataset.
 
+[vaecf_example.py](vaecf_example.py) - Variational Autoencoder for Collaborative Filtering (VAECF) with CiteULike dataset.
+
 [vbpr_tradesy.py](vbpr_tradesy.py) - Visual Bayesian Personalized Ranking (VBPR) with Tradesy dataset.
 
 [wmf_example.py](wmf_example.py) - Weighted Matrix Factorization with CiteULike dataset.

examples/vaecf_example.py

@@ -0,0 +1,38 @@
+# Copyright 2018 The Cornac Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Example for Variational Autoencoder for Collaborative Filtering for Implicit Feedback Datasets (Citeulike)"""
+
+import cornac
+from cornac.data import Reader
+from cornac.datasets import citeulike
+from cornac.eval_methods import RatioSplit
+
+_, item_ids = citeulike.load_text()
+
+data = citeulike.load_data(reader=Reader(item_set=item_ids))
+
+ratio_split = RatioSplit(data=data, test_size=0.2, exclude_unknowns=True,
+                         verbose=True, seed=123, rating_threshold=0.5)
+
+vaecf = cornac.models.vaecf.recom_vaecf.VAECF(name='VAECF', k=10, h=20, n_epochs=100, batch_size=100, learning_rate=0.001, beta=1.0, trainable=True, verbose=False, seed=123, use_gpu=False)
+
+rec_20 = cornac.metrics.Recall(k=20)
+ndcg_20 = cornac.metrics.NDCG(k=20)
+auc = cornac.metrics.AUC()
+
+cornac.Experiment(eval_method=ratio_split,
+                  models=[vaecf],
+                  metrics=[rec_20, ndcg_20, auc],
+                  user_based=True).run()