1. 数据解下载与配置

选择TMDB电影数据集，Netflix Prize 数据集下载。

也可直接从这里下载：链接: https://pan.baidu.com/s/1l6wjwcUzy5G_dIlVDbCkpw 提取码: pkq6 。

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执行preproc.py文件，进行数据预处理，生成5个处理后的文件：

import pandas as pd
import json
import redef Netflix(MAX_USER = 1000):d_movie = dict()s_movie = set()out_movies = open("../out_movies.csv","w")out_movies.write("title\n")for line in open("../movie_titles.csv","r",encoding = 'ISO-8859-1'):line = line.strip().split(',')movie_id = int(line[0])title = line[2].replace("\"","")title = "\"" + title + "\""d_movie[movie_id] = titleif title in s_movie:continues_movie.add(title)out_movies.write(f"{title}\n")out_movies.close()out_grade = open("../out_grade.csv","w")out_grade.write("user_id,title,grade\n")files = ["../combined_data_1.txt"]for f in files:movie_id = -1for line in open(f,"r"):pos = line.find(":")if pos != -1: # is a new usermovie_id = int(line[:pos])continueline = line.strip().split(",")user_id = int(line[0])rating = int(line[1])if user_id > MAX_USER:continueout_grade.write(f"{user_id},{d_movie[movie_id]},{rating}\n")out_grade.close()def TMDB():pattern = re.compile("[A-Za-z0-9]+")# 提取电影类型out_genre = open("../out_genre.csv","w",encoding='utf-8')out_genre.write("title,genre\n")# 提取电影关键词out_keyword = open("../out_keyword.csv","w",encoding='utf-8')out_keyword.write("title,keyword\n")# 提取电影制片人out_productor = open("../out_productor.csv","w",encoding='utf-8')out_productor.write("title,productor\n")df = pd.read_csv("../tmdb_5000_movies.csv", sep=",")json_columns = ['genres', 'keywords', 'production_companies']for column in json_columns:df[column] = df[column].apply(json.loads)df = df[["genres", "keywords", "original_title","production_companies"]]for _, row in df.iterrows():title = row["original_title"]if not pattern.fullmatch(title):continuetitle = "\"" + title + "\""for g in row["genres"]:genre = g["name"]genre = "\"" + genre + "\""out_genre.write(f"{title},{genre}\n")for g in row["keywords"]:keyword = g["name"]keyword = "\"" + keyword + "\""out_keyword.write(f"{title},{keyword}\n")for g in row["production_companies"]:productor = g["name"]productor = "\"" + productor + "\""out_productor.write(f"{title},{productor}\n")if __name__ == "__main__":Netflix()TMDB()

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2. 将处理好的数据导入数据库中

将上面数据预处理生成的5个文件，放入import文件夹中：

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3. 执行项目

修改main.py中的driver，输入自己数据库的用户名与密码。

执行main.py文件：

from neo4j import GraphDatabase
import pandas as pduri = "neo4j://localhost:7687"
driver = GraphDatabase.driver(uri, auth=("neo4j", "Liu881389."))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 similardef 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 csvCREATE (: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 csvMERGE (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 csvMERGE (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 csvMERGE (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 csvMERGE (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 gradeORDER 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)WITHu1, u2,COUNT(m) AS movies_common,SUM(r1.grading * r2.grading)/(SQRT(SUM(r1.grading^2)) * SQRT(SUM(r2.grading^2))) AS simWHERE 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, sORDER 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 genWHERE NOT((m)-[:RATED]-(u1)) {Q_GENRE}WITHm.title AS title,SUM(r.grading * s.sim)/SUM(s.sim) AS grade,COUNT(u2) AS num,genWHERE num >= {users_common}RETURN title, grade, num, genORDER BY grade DESC, num DESCLIMIT {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()continuedf = 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()