def personal_rank_matrix(graph, root, alpha, recom_num=10):
"""
:param graph: user item graph
:param root: the fix user to recom
:param alpha: the prob to random walk
:param recom_num: recom item num
:return: a dict, key itemid, value pr score
"""
m, vertex, address_dict = mat_util.graph_to_m(graph)
if root not in address_dict:
return {}
score_dict = {}
recom_dict = {}
mat_all = mat_util.mat_all_point(m, vertex, alpha)
index = address_dict[root]
initial_list = [[0] for i in range(len(vertex))]
initial_list[index] = [1]
r_zero = np.array(initial_list)
res = gmres(mat_all, r_zero, tol=1e-8)[0]
for i in range(len(res)):
point = vertex[i]
if len(point.strip().split('_')) < 2:
continue
if point in graph[root]:
continue
score_dict[point] = round(res[i], 3)
for co in sorted(score_dict.items(),
key=operator.itemgetter(1),
reverse=True)[:recom_num]:
point, score = co[0], co[1]
recom_dict[point] = score
return recom_dict
def personal_rank_mat(graph, root, alpha, recom_num=10):
"""
Args:
graph:user item graph
root:the fix user to recom
alpha:the prob to random walk
recom_num:recom item num
Return:
a dict, key: itemid, value: pr score
A*r = r0
"""
m, vertex, address_dict = mat_util.graph_to_m(graph)
vertex = np.array([v for v in vertex])
if root not in address_dict:
return {}
score_dict = {}
recom_dict = {}
mat_all = mat_util.mat_all_point(m, vertex, alpha)
index = address_dict[root]
initial_list = [[0] for row in range(len(vertex))]
initial_list[index] = [1]
r_zero = np.array(initial_list)
res = gmres(mat_all, r_zero, tol=1e-8)[0]
for index in range(len(res)):
point = vertex[index]
if len(point.strip().split("_")) < 2:
continue
if point in graph[root]:
continue
score_dict[point] = round(res[index], 3)
for zuhe in sorted(score_dict.items(), key=lambda v: v[1],
reverse=True)[:recom_num]:
point, score = zuhe[0], zuhe[1]
recom_dict[point] = score
return recom_dict
def personal_rank_mat(graph, root, alpha, recom_num=10):
"""
:param graph: user item graph 用户物品的二分图
:param root: the fix user to recom 固定用户推荐
:param alpha: the prob to random walk 随机游走的概率
:param recom_num: recom item num
:return: a dict, key :itemid ,value:pr score
线代相关知识：求矩阵的逆矩阵，即解线性方程 Ax = E （A*r = r0）
"""
m, vertex, address_dict = mat_util.graph_to_m(graph)
if root not in address_dict:
return {}
score_dict = {}
recom_dict = {}
# 求其逆，便可以得到推荐结果
mat_all = mat_util.mat_all_point(m, vertex, alpha)
# 首先得到root顶点的index,得到index的目的是为了获得r0矩阵
index = address_dict[root]
# 初始化r0矩阵
initial_list = [[0] for row in range(len(vertex))]
initial_list[index] = [1]
r_zero = np.array(initial_list)
# r_zero = np.concatenate(r_zero,axis=0)
# 解线性方程,得到的是一个元组，其中tol指的是误差
res = gmres(mat_all, r_zero, tol=1e-8)[0]
for index in range(len(res)):
# 首先判断该顶点是否是item顶点
point = vertex[index]
if len(point.strip().split("_")) < 2:
continue
# 若已经行为过，则也没有必要记录
if point in graph[root]:
continue
score_dict[point] = round(res[index], 3)
# 讲pr值排序，返回推荐结果
for zuhe in sorted(score_dict.items(),
key=operator.itemgetter(1),
reverse=True)[:recom_num]:
point, score = zuhe[0], zuhe[1]
recom_dict[point] = score
return recom_dict