neo4j_query.txt

# 유저 2명의유사도

MATCH (p1:User {user_id:1})-[r1:Has_rated]->(m:Movie)<-[r2:Has_rated]-(p2:User {user_id:2})
RETURN m.title AS Movie, r1.rating AS `u1's Rating`, r2.rating AS `u2's Rating`

# 유저 2명의 코사인 유사도 Insert

MATCH (p1:User {user_id:1})-[x:Has_rated]->(m:Movie)<-[y:Has_rated]-(p2:User {user_id:2})
WITH SUM(x.rating * y.rating) AS xyDotProduct,
 SQRT(REDUCE(xDot = 0.0, a IN COLLECT(x.rating) | xDot + a^2)) AS xLength,
 SQRT(REDUCE(yDot = 0.0, b IN COLLECT(y.rating) | yDot + b^2)) AS yLength,
 p1, p2
MERGE (p1)-[s:SIMILARITY]-(p2)
SET s.similarity = xyDotProduct / (xLength * yLength)

# 전체 유사도 Insert
MATCH (p1:User )-[x:Has_rated]->(m:Movie)<-[y:Has_rated]-(p2:User)
WITH SUM(x.rating * y.rating) AS xyDotProduct,
 SQRT(REDUCE(xDot = 0.0, a IN COLLECT(x.rating) | xDot + a^2)) AS xLength,
 SQRT(REDUCE(yDot = 0.0, b IN COLLECT(y.rating) | yDot + b^2)) AS yLength,
 p1, p2
MERGE (p1)-[s:SIMILARITY]-(p2)
SET s.similarity = xyDotProduct / (xLength * yLength)

# 사용자 간 유사도 조회
MATCH (p1:User {user_id:1})-[s:SIMILARITY]-(p2:User {user_id:2})
RETURN s.similarity AS 'Cosine Similarity'

# User1's KNN(5)

MATCH (p1:User {user_id: 1})-[s:SIMILARITY]-(p2:User)
WITH p2, s.similarity AS sim
ORDER BY sim DESC
LIMIT 5
RETURN p2.user_id AS Neighbor, sim AS Similarity