Merge branch 'master' into beacon

README.md

@@ -153,6 +153,7 @@ The recommender models supported by Cornac are listed below. Why don't you join
 |      | [Hybrid neural recommendation with joint deep representation learning of ratings and reviews (HRDR)](cornac/models/hrdr), [paper](https://www.sciencedirect.com/science/article/abs/pii/S0925231219313207) | [requirements.txt](cornac/models/hrdr/requirements.txt) | [hrdr_example.py](examples/hrdr_example.py)
 |      | [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](cornac/models/lightgcn), [paper](https://arxiv.org/pdf/2002.02126.pdf) | [requirements.txt](cornac/models/lightgcn/requirements.txt) | [lightgcn_example.py](examples/lightgcn_example.py)
 |      | [New Variational Autoencoder for Top-N Recommendations with Implicit Feedback (RecVAE)](cornac/models/recvae), [paper](https://doi.org/10.1145/3336191.3371831) | [requirements.txt](cornac/models/recvae/requirements.txt) | [recvae_example.py](examples/recvae_example.py)
+|      | [Recency Aware Collaborative Filtering for Next Basket Recommendation (UPCF)](cornac/models/upcf), [paper](https://dl.acm.org/doi/abs/10.1145/3340631.3394850) | [requirements.txt](cornac/models/upcf/requirements.txt) | [upcf_tafeng.py](examples/upcf_tafeng.py)
 |      | [Temporal-Item-Frequency-based User-KNN (TIFUKNN)](cornac/models/tifuknn), [paper](https://arxiv.org/pdf/2006.00556.pdf) | N/A | [tifuknn_tafeng.py](examples/tifuknn_tafeng.py)
 | 2019 | [Correlation-Sensitive Next-Basket Recommendation (Beacon)](cornac/models/beacon), [paper](https://www.ijcai.org/proceedings/2019/0389.pdf) | [requirements.txt](cornac/models/beacon/requirements.txt) | [beacon_tafeng.py](examples/beacon_tafeng.py)
 |      | [Embarrassingly Shallow Autoencoders for Sparse Data (EASEᴿ)](cornac/models/ease), [paper](https://arxiv.org/pdf/1905.03375.pdf) | N/A | [ease_movielens.py](examples/ease_movielens.py)

cornac/models/__init__.py

@@ -75,6 +75,7 @@
 from .svd import SVD
 from .tifuknn import TIFUKNN
 from .trirank import TriRank
+from .upcf import UPCF
 from .vaecf import VAECF
 from .vbpr import VBPR
 from .vmf import VMF

cornac/models/upcf/__init__.py

@@ -0,0 +1,16 @@
+# Copyright 2023 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.
+# ============================================================================
+
+from .recom_upcf import UPCF

cornac/models/upcf/recom_upcf.py

@@ -0,0 +1,133 @@
+# Copyright 2023 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.
+# ============================================================================
+
+import itertools
+
+import numpy as np
+from scipy.sparse import csr_matrix, vstack
+
+from ..recommender import NextBasketRecommender
+
+
+class UPCF(NextBasketRecommender):
+    """User Popularity-based CF (UPCF)
+
+    Parameters
+    ----------
+    name: string, default: 'UPCF'
+        The name of the recommender model.
+
+    recency: int, optional, default: 1
+        The size of recency window.
+        If 0, all baskets will be used.
+
+    locality: int, optional, default: 1
+        The strength we enforce the similarity between two items within a basket
+
+    asymmetry: float, optional, default: 0.25
+        Trade-off parameter which balances the importance of the probability of having item i given j and probability having item j given i.
+        This value will be computed via `similaripy.asymetric_cosine`.
+
+    verbose: boolean, optional, default: False
+        When True, running logs are displayed.
+
+    References
+    ----------
+    Guglielmo Faggioli, Mirko Polato, and Fabio Aiolli. 2020.
+    Recency Aware Collaborative Filtering for Next Basket Recommendation.
+    In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (UMAP '20). Association for Computing Machinery, New York, NY, USA, 80–87. https://doi.org/10.1145/3340631.3394850
+
+    """
+
+    def __init__(
+        self,
+        name="UPCF",
+        recency=1,
+        locality=1,
+        asymmetry=0.25,
+        verbose=False,
+    ):
+        super().__init__(name=name, trainable=False, verbose=verbose)
+        self.recency = recency
+        self.locality = locality
+        self.asymmetry = asymmetry
+
+    def fit(self, train_set, val_set=None):
+        super().fit(train_set=train_set, val_set=val_set)
+        self.user_wise_popularity = vstack(
+            [
+                self._get_user_wise_popularity(basket_items)
+                for _, _, [basket_items] in train_set.ubi_iter(
+                    batch_size=1, shuffle=False
+                )
+            ]
+        )
+        (u_indices, i_indices, r_values) = train_set.uir_tuple
+        self.user_item_matrix = csr_matrix(
+            (r_values, (u_indices, i_indices)),
+            shape=(train_set.num_users, self.total_items),
+            dtype="float32",
+        )
+        return self
+
+    def _get_user_wise_popularity(self, basket_items):
+        users = []
+        items = []
+        scores = []
+        recent_basket_items = (
+            basket_items[-self.recency :] if self.recency > 0 else basket_items
+        )
+        for iid in list(set(itertools.chain.from_iterable(recent_basket_items))):
+            users.append(0)
+            items.append(iid)
+            denominator = (
+                min(self.recency, len(recent_basket_items))
+                if self.recency > 0
+                else len(recent_basket_items)
+            )
+            numerator = sum([1 for items in recent_basket_items if iid in items])
+            scores.append(numerator / denominator)
+        return csr_matrix(
+            (scores, (users, items)), shape=(1, self.total_items), dtype="float32"
+        )
+
+    def score(self, user_idx, history_baskets, **kwargs):
+        import similaripy as sim
+
+        items = list(set(itertools.chain.from_iterable(history_baskets)))
+        current_user_item_matrix = csr_matrix(
+            (np.ones(len(items)), (np.zeros(len(items)), items)),
+            shape=(1, self.total_items),
+            dtype="float32",
+        )
+        current_user_wise_popularity = self._get_user_wise_popularity(history_baskets)
+        user_wise_popularity = vstack(
+            [current_user_wise_popularity, self.user_wise_popularity]
+        )
+        user_item_matrix = vstack([current_user_item_matrix, self.user_item_matrix])
+        user_sim = sim.asymmetric_cosine(
+            user_item_matrix, alpha=self.asymmetry, target_rows=[0], verbose=False
+        )
+        scores = (
+            sim.dot_product(
+                user_sim.power(self.locality).tocsr()[0],
+                user_wise_popularity,
+                verbose=False,
+            )
+            .toarray()
+            .squeeze()
+        )
+
+        return scores

cornac/models/upcf/requirements.txt

@@ -0,0 +1 @@
+similaripy==0.1.3

examples/README.md

@@ -123,3 +123,5 @@
 [beacon_tafeng.py](beacon_tafeng.py) - Correlation-Sensitive Next-Basket Recommendation (Beacon).
 
 [tifuknn_tafeng.py](tifuknn_tafeng.py) - Example of Temporal-Item-Frequency-based User-KNN (TIFUKNN).
+
+[upcf_tafeng.py](upcf_tafeng.py) - Example of Recency Aware Collaborative Filtering for Next Basket Recommendation (UPCF).

examples/upcf_tafeng.py

@@ -0,0 +1,50 @@
+# Copyright 2023 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 of Recency Aware Collaborative Filtering for Next Basket Recommendation (UPCF)"""
+
+import cornac
+from cornac.eval_methods import NextBasketEvaluation
+from cornac.metrics import NDCG, HitRatio, Recall
+from cornac.models import UPCF
+
+data = cornac.datasets.tafeng.load_basket(
+    reader=cornac.data.Reader(
+        min_basket_size=3, max_basket_size=50, min_basket_sequence=2
+    )
+)
+
+next_basket_eval = NextBasketEvaluation(
+    data=data, fmt="UBITJson", test_size=0.2, val_size=0.08, seed=123, verbose=True
+)
+
+models = [
+    UPCF(
+        recency=1,
+        locality=1,
+        asymmetry=0.25,
+        verbose=True,
+    )
+]
+
+metrics = [
+    Recall(k=10),
+    Recall(k=50),
+    NDCG(k=10),
+    NDCG(k=50),
+    HitRatio(k=10),
+    HitRatio(k=50),
+]
+
+cornac.Experiment(eval_method=next_basket_eval, models=models, metrics=metrics).run()