首先下载neo4j图数据库
https://neo4j.com/deployment-center/
知识图谱相关概念：
知识图谱是一种结构化的知识存储形式，它以图形数据库为基础，用来表示实体（如人、地点、事件等）之间的复杂关系。这种技术结合了自然语言处理、信息提取、数据挖掘、图论等多种技术，目的是使机器能够更好地理解和处理人类世界中的知识。
完整代码：
from neo4j import GraphDatabase
import pandas as pd
uri = "neo4j://localhost:7687"
driver = GraphDatabase.driver(uri, auth=("neo4j", "tangyudiadid0"))
k = 10 # nearest neighbors (most similar users) to consider
movies_common = 3 # how many movies in common to be consider an user similar
users_common = 2 # minimum number of similar users that have seen the movie to consider it
threshold_sim = 0.9 # threshold to consider users similar
def load_data():
with driver.session() as session:
session.run("""MATCH ()-[r]->() DELETE r""")
session.run("""MATCH (r) DELETE r""")
print("Loading movies...")
#加载数据，创建Movie标签,title属性的实体
session.run("""
LOAD CSV WITH HEADERS FROM "file:///out_movies.csv" AS csv
CREATE (:Movie {title: csv.title})
""")
print("Loading gradings...")
#加载评分数据， MERGE是搜索给定模式，如果存在，则返回结果如果它不存在于图中，则它创建新的节点/关系并返回结果。
session.run("""
LOAD CSV WITH HEADERS FROM "file:///out_grade.csv" AS csv
MERGE (m:Movie {title: csv.title})
MERGE (u:User {id: toInteger(csv.user_id)})
CREATE (u)-[:RATED {grading : toInteger(csv.grade)}]->(m)
""")
#加载影片类型数据
print("Loading genres...")
session.run("""
LOAD CSV WITH HEADERS FROM "file:///out_genre.csv" AS csv
MERGE (m:Movie {title: csv.title})
MERGE (g:Genre {genre: csv.genre})
CREATE (m)-[:HAS_GENRE]->(g)
""")
print("Loading keywords...")
#加载关键词数据
session.run("""
LOAD CSV WITH HEADERS FROM "file:///out_keyword.csv" AS csv
MERGE (m:Movie {title: csv.title})
MERGE (k:Keyword {keyword: csv.keyword})
CREATE (m)-[:HAS_KEYWORD]->(k)
""")
print("Loading productors...")
#制片人
session.run("""
LOAD CSV WITH HEADERS FROM "file:///out_productor.csv" AS csv
MERGE (m:Movie {title: csv.title})
MERGE (p:Productor {name: csv.productor})
CREATE (m)-[:HAS_PRODUCTOR]->(p)
""")
def queries():
while True:
userid = int(input("请输入要为哪位用户推荐电影，输入其ID即可: "))
m = int(input("为该用户推荐多少个电影呢？ "))
genres = []
if int(input("是否需要过滤掉不喜欢的类型?（输入0或1）")):#过滤掉不喜欢的类型
with driver.session() as session:
try:
q = session.run(f"""MATCH (g:Genre) RETURN g.genre AS genre""")
result = []
for i, r in enumerate(q):
result.append(r["genre"])#找到图谱中所有的电影类型
df = pd.DataFrame(result, columns=["genre"])
print()
print(df)
inp = input("输入不喜欢的类型索引即可，例如：1 2 3 ")
if len(inp) != 0:
inp = inp.split(" ")
genres = [df["genre"].iloc[int(x)] for x in inp]
except:
print("Error")
with driver.session() as session:#找到当前ID评分的电影
q = session.run(f"""
MATCH (u1:User {{id : {userid}}})-[r:RATED]-(m:Movie)
RETURN m.title AS title, r.grading AS grade
ORDER BY grade DESC
""")
print()
print("Your ratings are the following:")
result = []
for r in q:
result.append([r["title"], r["grade"]])
if len(result) == 0:
print("No ratings found")
else:
df = pd.DataFrame(result, columns=["title", "grade"])
print()
print(df.to_string(index=False))
print()
session.run(f"""
MATCH (u1:User)-[s:SIMILARITY]-(u2:User)
DELETE s
""")
#找到当前用户评分的电影以及这些电影被其他用户评分的用户，with是把查询集合当做结果以便后面用where 余弦相似度计算
session.run(f"""
MATCH (u1:User {{id : {userid}}})-[r1:RATED]-(m:Movie)-[r2:RATED]-(u2:User)
WITH
u1, u2,
COUNT(m) AS movies_common,
SUM(r1.grading * r2.grading)/(SQRT(SUM(r1.grading^2)) * SQRT(SUM(r2.grading^2))) AS sim
WHERE movies_common >= {movies_common} AND sim > {threshold_sim}
MERGE (u1)-[s:SIMILARITY]-(u2)
SET s.sim = sim
""")
Q_GENRE = ""
if (len(genres) > 0):
Q_GENRE = "AND ((SIZE(gen) > 0) AND "
Q_GENRE += "(ANY(x IN " + str(genres) + " WHERE x IN gen))"
Q_GENRE += ")"
#找到相似的用户，然后看他们喜欢什么电影 Collect：将所有值收集到一个集合list中
q = session.run(f"""
MATCH (u1:User {{id : {userid}}})-[s:SIMILARITY]-(u2:User)
WITH u1, u2, s
ORDER BY s.sim DESC LIMIT {k}
MATCH (m:Movie)-[r:RATED]-(u2)
OPTIONAL MATCH (g:Genre)--(m)
WITH u1, u2, s, m, r, COLLECT(DISTINCT g.genre) AS gen
WHERE NOT((m)-[:RATED]-(u1)) {Q_GENRE}
WITH
m.title AS title,
SUM(r.grading * s.sim)/SUM(s.sim) AS grade,
COUNT(u2) AS num,
gen
WHERE num >= {users_common}
RETURN title, grade, num, gen
ORDER BY grade DESC, num DESC
LIMIT {m}
""")
print("Recommended movies:")
result = []
for r in q:
result.append([r["title"], r["grade"], r["num"], r["gen"]])
if len(result) == 0:
print("No recommendations found")
print()
continue
df = pd.DataFrame(result, columns=["title", "avg grade", "num recommenders", "genres"])
print()
print(df.to_string(index=False))
print()
if __name__ == "__main__":
if int(input("是否需要重新加载并创建知识图谱？（请选择输入0或1）")):
load_data()
queries()
运行效果：
后注：有疑问的小伙伴请访问公众号“聚财猫猫”