一.介绍：
　　　The Jaccard Similarity algorithm，杰卡德相似性算法，主要用来计算样本集合之间的相似度。
　　　给定两个集合Ａ，Ｂ，jaccard 系数定义为A与B交集的大小与并集大小的比值。
　　　公式描述为：
　　　
　　　杰卡德值越大，说明集合之间相似度越大。
二.neo4j算法：

CALL algo.similarity.jaccard.stream(userData:List<Map>）
注意在求相似度时，两个样本应放在集合内。

三.实例：

MERGE (french:Cuisine {name:'French'})
MERGE (italian:Cuisine {name:'Italian'})
MERGE (indian:Cuisine {name:'Indian'})
MERGE (lebanese:Cuisine {name:'Lebanese'})
MERGE (portuguese:Cuisine {name:'Portuguese'})
MERGE (zhen:Person {name: "Zhen"})
MERGE (praveena:Person {name: "Praveena"})
MERGE (michael:Person {name: "Michael"})
MERGE (arya:Person {name: "Arya"})
MERGE (karin:Person {name: "Karin"})
MERGE (shrimp:Recipe {title: "Shrimp Bolognese"})
MERGE (saltimbocca:Recipe {title: "Saltimbocca alla roman"})
MERGE (periperi:Recipe {title: "Peri Peri Naan"})
MERGE (praveena)-[:LIKES]->(indian)
MERGE (praveena)-[:LIKES]->(portuguese)
MERGE (zhen)-[:LIKES]->(french)
MERGE (zhen)-[:LIKES]->(indian)
MERGE (michael)-[:LIKES]->(french)
MERGE (michael)-[:LIKES]->(italian)
MERGE (michael)-[:LIKES]->(indian)
MERGE (arya)-[:LIKES]->(lebanese)
MERGE (arya)-[:LIKES]->(italian)
MERGE (arya)-[:LIKES]->(portuguese)
MERGE (karin)-[:LIKES]->(lebanese)
MERGE (karin)-[:LIKES]->(italian)
MERGE (shrimp)-[:TYPE]->(italian)
MERGE (shrimp)-[:TYPE]->(indian)
MERGE (saltimbocca)-[:TYPE]->(italian)
MERGE (saltimbocca)-[:TYPE]->(french)
MERGE (periperi)-[:TYPE]->(portuguese)
MERGE (periperi)-[:TYPE]->(indian)

求单个节点之间的相似度：

MATCH (p1:Person {name: 'Karin'})-[:LIKES]->(cuisine1)
WITH p1, collect(id(cuisine1)) AS p1Cuisine
MATCH (p2:Person {name: "Arya"})-[:LIKES]->(cuisine2)
WITH p1, p1Cuisine, p2, collect(id(cuisine2)) AS p2Cuisine
RETURN p1.name AS from,
p2.name AS to,
algo.similarity.jaccard(p1Cuisine, p2Cuisine) AS similarity

求所有节点之间的相似度：

MATCH (p:Person)-[:LIKES]->(cuisine)
WITH {item:id(p), categories: collect(id(cuisine))} as userData
WITH collect(userData) as data
CALL algo.similarity.jaccard.stream(data)
YIELD item1, item2, count1, count2, intersection, similarity
RETURN algo.getNodeById(item1).name AS from, algo.getNodeById(item2).name AS to, intersection,
similarity
ORDER BY similarity DESC

其中intersection为两个集合之间节点重叠个数。