From 6b42eea9c9d04302c6de69c1b11e00663692881a Mon Sep 17 00:00:00 2001
From: syk1209wwz0306 <3228195956@qq.com>
Date: Sun, 2 Jun 2024 22:49:36 +0800
Subject: [PATCH] commit0602

---
 .../movie/__pycache__/views.cpython-38.pyc    | Bin 3023 -> 5765 bytes
 MoviesRecommend/movie/views.py                | 107 +++++++++++++++++-
 2 files changed, 106 insertions(+), 1 deletion(-)

diff --git a/MoviesRecommend/movie/__pycache__/views.cpython-38.pyc b/MoviesRecommend/movie/__pycache__/views.cpython-38.pyc
index bbad21ca0bcfbb62d025dd207fa61ca84f1973f5..f8ef47137db6349fabcd92af5ea21c4d30e2bcb6 100644
GIT binary patch
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diff --git a/MoviesRecommend/movie/views.py b/MoviesRecommend/movie/views.py
index 1a92159..4260706 100644
--- a/MoviesRecommend/movie/views.py
+++ b/MoviesRecommend/movie/views.py
@@ -105,4 +105,109 @@ class SearchView(ListView):
             'current_page': current_page,
             'left_has_more': left_has_more,
             'right_has_more': right_has_more
-        }
\ No newline at end of file
+        }
+
+    # 推荐电影视图
+    class RecommendMovieView(ListView):
+        model = Movie
+        template_name = 'movie/recommend.html'
+        paginate_by = 15
+        context_object_name = 'movies'
+        ordering = 'movie_rating__score'
+        page_kwarg = 'p'
+
+        def __init__(self):
+            super().__init__()
+            # 最相似的20个用户
+            self.K = 20
+            # 推荐出10本书
+            self.N = 10
+            # 存放当前用户评分过的电影querySet
+            self.cur_user_movie_qs = None
+
+        # 获取用户相似度
+        def get_user_sim(self):
+            # 用户相似度字典，格式为{ user_id1:val , user_id2:val , ... }
+            user_sim_dct = dict()
+            '''获取用户之间的相似度,存放在user_sim_dct中'''
+            # 获取当前用户
+            cur_user_id = self.request.session['user_id']
+            cur_user = User.objects.get(pk=cur_user_id)
+            # 获取其它用户
+            other_users = User.objects.exclude(pk=cur_user_id)  # 除了当前用户外的所有用户
+
+            # 当前用户评分过的电影
+            self.cur_user_movie_qs = Movie.objects.filter(user=cur_user)
+
+            # 计算当前用户与其他用户共同评分过的电影交集数
+            for user in other_users:
+                # 记录感兴趣的数量
+                user_sim_dct[user.id] = len(Movie.objects.filter(user=user) & self.cur_user_movie_qs)
+
+            # 按照key排序value，返回K个最相近的用户（共同评分过的电影交集数更多）
+            print("user similarity calculated!")
+            # 格式 [ (user, value), (user, value), ... ]
+            return sorted(user_sim_dct.items(), key=lambda x: -x[1])[:self.K]
+
+        # 获取推荐电影（按照相似用户总得分排序）
+        def get_recommend_movie(self, user_lst):
+            # 电影兴趣值字典，{ movie:value, movie:value , ...}
+            movie_val_dct = dict()
+            # 用户，相似度
+            for user, _ in user_lst:
+                # 获取相似用户评分过的电影，并且不在前用户的评分列表中的，再加上score字段，方便计算兴趣值
+                movie_set = Movie.objects.filter(user=user).exclude(id__in=self.cur_user_movie_qs).annotate(
+                    score=Max('movie_rating__score'))
+                for movie in movie_set:
+                    movie_val_dct.setdefault(movie, 0)
+                    # 累计用户的评分
+                    movie_val_dct[movie] += movie.score
+            return sorted(movie_val_dct.items(), key=lambda x: -x[1])[:self.N]
+
+        # 获取数据
+        def get_queryset(self):
+            s = time.time()
+            # 获得最相似的K个用户列表
+            user_lst = self.get_user_sim()
+            # 获得推荐电影的id
+            movie_lst = self.get_recommend_movie(user_lst)
+            # print(movie_lst)
+            result_lst = []
+            for movie, _ in movie_lst:
+                result_lst.append(movie)
+            e = time.time()
+            print(f"算法推荐用时:{e - s}秒！")
+            return result_lst
+
+        def get_context_data(self, *, object_list=None, **kwargs):
+            context = super(RecommendMovieView, self).get_context_data(*kwargs)
+            print(context)
+            paginator = context.get('paginator')
+            page_obj = context.get('page_obj')
+            pagination_data = self.get_pagination_data(paginator, page_obj)
+            context.update(pagination_data)
+            return context
+
+        def get_pagination_data(self, paginator, page_obj, around_count=2):
+            current_page = page_obj.number
+
+            if current_page <= around_count + 2:
+                left_pages = range(1, current_page)
+                left_has_more = False
+            else:
+                left_pages = range(current_page - around_count, current_page)
+                left_has_more = True
+
+            if current_page >= paginator.num_pages - around_count - 1:
+                right_pages = range(current_page + 1, paginator.num_pages + 1)
+                right_has_more = False
+            else:
+                right_pages = range(current_page + 1, current_page + 1 + around_count)
+                right_has_more = True
+            return {
+                'left_pages': left_pages,
+                'right_pages': right_pages,
+                'current_page': current_page,
+                'left_has_more': left_has_more,
+                'right_has_more': right_has_more
+            }