Added UserKNN and ItemKNN methods (#329)

README.md

@@ -158,13 +158,15 @@ The recommender models supported by Cornac are listed below. Why don't you join
 | Earlier | [Baseline Only](cornac/models/baseline_only), [paper](http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/a1-koren.pdf) | N/A | [svd_exp.py](examples/svd_example.py)
 |      | [Bayesian Personalized Ranking (BPR)](cornac/models/bpr), [paper](https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf) | N/A | [bpr_netflix.py](examples/bpr_netflix.py)
 |      | [Global Average (GlobalAvg)](cornac/models/global_avg), [paper](https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf) | N/A | [biased_mf.py](examples/biased_mf.py)
+|      | [Item K-Nearest-Neighbors (ItemKNN)](cornac/models/knn), [paper](https://dl.acm.org/doi/pdf/10.1145/371920.372071) | N/A | [knn_movielens.py](examples/knn_movielens.py)
 |      | [Matrix Factorization (MF)](cornac/models/mf), [paper](https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf) | N/A | [biased_mf.py](examples/biased_mf.py), [given_data.py](examples/given_data.py)
 |      | [Maximum Margin Matrix Factorization (MMMF)](cornac/models/mmmf), [paper](https://link.springer.com/content/pdf/10.1007/s10994-008-5073-7.pdf) | N/A | [mmmf_exp.py](examples/mmmf_exp.py)
 |      | [Most Popular (MostPop)](cornac/models/most_pop), [paper](https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf) | N/A | [bpr_netflix.py](examples/bpr_netflix.py)
 |      | [Non-negative Matrix Factorization (NMF)](cornac/models/nmf), [paper](http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf) | N/A | [nmf_exp.py](examples/nmf_example.py)
 |      | [Probabilistic Matrix Factorization (PMF)](cornac/models/pmf), [paper](https://papers.nips.cc/paper/3208-probabilistic-matrix-factorization.pdf) | N/A | [pmf_ratio.py](examples/pmf_ratio.py)
 |      | [Singular Value Decomposition (SVD)](cornac/models/svd), [paper](https://www.cs.rochester.edu/twiki/pub/Main/HarpSeminar/Factorization_Meets_the_Neighborhood-_a_Multifaceted_Collaborative_Filtering_Model.pdf) | N/A | [svd_exp.py](examples/svd_example.py)
 |      | [Social Recommendation using PMF (SoRec)](cornac/models/sorec), [paper](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.304.2464&rep=rep1&type=pdf) | N/A | [sorec_filmtrust.py](examples/sorec_filmtrust.py)
+|      | [User K-Nearest-Neighbors (UserKNN)](cornac/models/knn), [paper](https://arxiv.org/pdf/1301.7363.pdf) | N/A | [knn_movielens.py](examples/knn_movielens.py)
 |      | [Weighted Matrix Factorization (WMF)](cornac/models/wmf), [paper](http://yifanhu.net/PUB/cf.pdf) | [requirements.txt](cornac/models/wmf/requirements.txt) | [wmf_exp.py](examples/wmf_example.py)
 
 ## Support

cornac/models/__init__.py

@@ -30,6 +30,8 @@
 from .hft import HFT
 from .hpf import HPF
 from .ibpr import IBPR
+from .knn import ItemKNN
+from .knn import UserKNN
 from .mcf import MCF
 from .mf import MF
 from .mmmf import MMMF

cornac/models/knn/__init__.py

@@ -0,0 +1,17 @@
+# 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.
+# ============================================================================
+
+from .recom_knn import ItemKNN
+from .recom_knn import UserKNN