Neo4j Cypher：使用公共节点关系链接为医生推荐文章

用例

我的应用程序应该根据用户的个人资料和用户操作向用户（医生）推送文章。在下面的示例中，考虑（参见下面的图表）：

• 两位医生（一位是职业：医生，专业：肿瘤学，另一位是医生，专业：未知）
• 一篇文章已手动链接到专业：医生、专业：肿瘤学和关键词：膝关节手术（即，向匹配此配置文件的用户显示本文）

前端向后端发送用户id，后端需要使用与用户配置文件匹配的

n

文章进行响应

问题

给定一个用户配置文件，Neo4jCypher查询基本上会转换为

MATCH（a:Article）…

。或者用不同的方式查找与用户拥有的职业、关键字和专业节点具有类似关系的（a:Article）[……]

我的密码查询应该是什么形状？

TL；DR

这是一个开始：

MATCH p=(u:User {id: $theID})-[:r]->()<-[:m]-(a:Article)
WITH a, COUNT(p) as rank
RETURN a.name, rank
ORDER BY rank DESC
LIMIT $n

这是错综复杂的混乱局面

现在只看一个“医生”：

---

MATCH p=（u:User{name:“Barney”}）-[:R]->（）（你知道如何只使用
:Keyword
，
:Profession
，或
:speciality
中的一个来查找
:User
？我知道如何匹配基本路径，如MATCH（a:Article）-[]-（n）[]。。。等等，但在这里我想找到一个给定用户的“精确匹配”文章，考虑到两者之间的关系（其中文章与用户的关系最多），你有一些测试数据要共享吗？只是你在屏幕上看到的数据。如果需要的话，我可以导出create语句。在我编辑了你的帖子后，我投了赞成票。我想大多数人都喜欢看MWE，即使它是失败的。。。或者是给定输入的所需输出的清晰示例等。一些示例数据的来源也为解决方案寻求者节省了大量时间。此外，这里也是索取密码的好地方。他们也喜欢用例。
MERGE (p:Profession {name: "Doctor"})
WITH p, ["Fred","Wilma","Pebbles","Dino","Barney","Betty","Bamm-bamm", "Hoppy"] as doctors
UNWIND doctors as doc
MERGE (d:User {name: doc})-[:R]->(p);

WITH ["Oncology", "Pathology","Pediatrics","ENT","Radiology","Dermatology"] as specialties
UNWIND specialties as spec
MERGE (:Specialty {name: spec});

MATCH (d:User)
WITH d, ["Oncology", "Pathology","Pediatrics","ENT","Radiology","Dermatology"] as specialties
UNWIND apoc.coll.randomItems(specialties, toInteger(apoc.text.random(1,"00111223"))) as docspec
MATCH (spec:Specialty {name: docspec})
MERGE (d)-[:R]->(spec);

WITH ["Knee Surgery","Ear Injury","Throat Cultures","Radial fractures", "Arm rash", "Mole color"] as keywords
UNWIND keywords as key
MERGE (:Keyword {name: key});

MATCH (d:User)
WITH d, ["Knee Surgery","Ear Injury","Throat Cultures","Radial fractures", "Arm rash", "Mole color"] as keywords
UNWIND apoc.coll.randomItems(keywords, toInteger(apoc.text.random(1,"01112233"))) as docword
MATCH (key:Keyword {name: docword})
MERGE (d)-[:R]->(key);

MATCH (p:Profession {name: "Doctor"})
WITH p, ["Useful article","Cool article","Awesome article","Research article","Nifty article","Health article"] as articles
UNWIND articles as art
MERGE (:Article {name: art})-[:M]->(p);

MATCH (art:Article)
WITH art, ["Oncology", "Pathology","Pediatrics","ENT","Radiology","Dermatology"] as specialties
UNWIND apoc.coll.randomItems(specialties, toInteger(apoc.text.random(1,"111223"))) as artspec
MATCH (spec:Specialty {name: artspec})
MERGE (art)-[:M]->(spec);

MATCH (art:Article)
WITH art, ["Knee Surgery","Ear Injury","Throat Cultures","Radial fractures", "Arm rash", "Mole color"] as keywords
UNWIND apoc.coll.randomItems(keywords, toInteger(apoc.text.random(1,"1112233"))) as artword
MATCH (key:Keyword {name: artword})
MERGE (art)-[:M]->(key);

MATCH p=(u:User {name: "Barney"})-[:R]->()<-[:M]-(a:Article)
RETURN p

MATCH p=(u:User {name: "Barney"})-[:R]->()<-[:M]-(a:Article)
WITH a, COUNT(p) as rank
RETURN a.name, rank
ORDER BY rank DESC