neo4j cypher

Neo4j的最常見用途之一是構(gòu)建實時推薦引擎，一個共同的主題是它們利用大量不同的數(shù)據(jù)來提出有趣的推薦。

例如， 在此視頻中， 阿曼達（Amanda）展示了約會網(wǎng)站如何通過社交聯(lián)系開始，然后介紹熱情，位置和其他一些東西，從而構(gòu)建實時推薦引擎。

Graph Aware有一個簡潔的框架 ，可以幫助您使用Java構(gòu)建自己的推薦引擎，我很好奇Cypher版本的外觀。

這是示例圖：

CREATE(m:Person:Male {name:'Michal', age:30}),(d:Person:Female {name:'Daniela', age:20}),(v:Person:Male {name:'Vince', age:40}),(a:Person:Male {name:'Adam', age:30}),(l:Person:Female {name:'Luanne', age:25}),(c:Person:Male {name:'Christophe', age:60}),(lon:City {name:'London'}),(mum:City {name:'Mumbai'}),(m)-[:FRIEND_OF]->(d),(m)-[:FRIEND_OF]->(l),(m)-[:FRIEND_OF]->(a),(m)-[:FRIEND_OF]->(v),(d)-[:FRIEND_OF]->(v),(c)-[:FRIEND_OF]->(v),(d)-[:LIVES_IN]->(lon),(v)-[:LIVES_IN]->(lon),(m)-[:LIVES_IN]->(lon),(l)-[:LIVES_IN]->(mum);

我們想向“亞當(dāng)”推薦一些潛在的朋友，因此我們查詢的第一層是找到他的朋友，因為其中肯定有一些潛在的朋友：

MATCH (me:Person {name: "Adam"}) MATCH (me)-[:FRIEND_OF]-()-[:FRIEND_OF]-(potentialFriend) RETURN me, potentialFriend, COUNT(*) AS friendsInCommon==> +--------------------------------------------------------------------------------------+ ==> | me | potentialFriend | friendsInCommon | ==> +--------------------------------------------------------------------------------------+ ==> | Node[1007]{name:"Adam",age:30} | Node[1006]{name:"Vince",age:40} | 1 | ==> | Node[1007]{name:"Adam",age:30} | Node[1005]{name:"Daniela",age:20} | 1 | ==> | Node[1007]{name:"Adam",age:30} | Node[1008]{name:"Luanne",age:25} | 1 | ==> +--------------------------------------------------------------------------------------+ ==> 3 rows

該查詢?yōu)槲覀兲峁┝藵撛谂笥训牧斜硪约拔覀冇卸嗌賯€共同的朋友。

現(xiàn)在我們有了一些潛在的朋友，讓我們開始為他們每個人建立一個排名。 一個可以吸引潛在朋友的指標(biāo)是，如果他們和我們生活在同一地點，那么可以將其添加到查詢中：

MATCH (me:Person {name: "Adam"}) MATCH (me)-[:FRIEND_OF]-()-[:FRIEND_OF]-(potentialFriend)WITH me, potentialFriend, COUNT(*) AS friendsInCommonRETURN me,potentialFriend,SIZE((potentialFriend)-[:LIVES_IN]->()<-[:LIVES_IN]-(me)) AS sameLocation==> +-----------------------------------------------------------------------------------+ ==> | me | potentialFriend | sameLocation | ==> +-----------------------------------------------------------------------------------+ ==> | Node[1007]{name:"Adam",age:30} | Node[1006]{name:"Vince",age:40} | 0 | ==> | Node[1007]{name:"Adam",age:30} | Node[1005]{name:"Daniela",age:20} | 0 | ==> | Node[1007]{name:"Adam",age:30} | Node[1008]{name:"Luanne",age:25} | 0 | ==> +-----------------------------------------------------------------------------------+ ==> 3 rows

接下來，我們將通過比較每個節(jié)點的標(biāo)簽來檢查Adams的潛在朋友是否與他具有相同的性別。 我們提供了“性別”和“性別”標(biāo)簽。

MATCH (me:Person {name: "Adam"}) MATCH (me)-[:FRIEND_OF]-()-[:FRIEND_OF]-(potentialFriend)WITH me, potentialFriend, COUNT(*) AS friendsInCommonRETURN me,potentialFriend,SIZE((potentialFriend)-[:LIVES_IN]->()<-[:LIVES_IN]-(me)) AS sameLocation,LABELS(me) = LABELS(potentialFriend) AS gender==> +--------------------------------------------------------------------------------------------+ ==> | me | potentialFriend | sameLocation | gender | ==> +--------------------------------------------------------------------------------------------+ ==> | Node[1007]{name:"Adam",age:30} | Node[1006]{name:"Vince",age:40} | 0 | true | ==> | Node[1007]{name:"Adam",age:30} | Node[1005]{name:"Daniela",age:20} | 0 | false | ==> | Node[1007]{name:"Adam",age:30} | Node[1008]{name:"Luanne",age:25} | 0 | false | ==> +--------------------------------------------------------------------------------------------+ ==> 3 rows

接下來，讓我們計算亞當(dāng)和他的潛在朋友之間的年齡差異：

MATCH (me:Person {name: "Adam"}) MATCH (me)-[:FRIEND_OF]-()-[:FRIEND_OF]-(potentialFriend)WITH me, potentialFriend, COUNT(*) AS friendsInCommonRETURN me,potentialFriend,SIZE((potentialFriend)-[:LIVES_IN]->()<-[:LIVES_IN]-(me)) AS sameLocation,abs( me.age - potentialFriend.age) AS ageDifference,LABELS(me) = LABELS(potentialFriend) AS gender,friendsInCommon==> +--------------------------------------------------------------------------------------+ ==> | me | potentialFriend | sameLocation | ageDifference | gender | friendsInCommon | ==> +--------------------------------------------------------------------------------------+ ==> | Node[1007]{name:"Adam",age:30} | Node[1006]{name:"Vince",age:40} | 0 | 10.0 | true | 1 | ==> | Node[1007]{name:"Adam",age:30} | Node[1005]{name:"Daniela",age:20} | 0 | 10.0 | false | 1 | ==> | Node[1007]{name:"Adam",age:30} | Node[1008]{name:"Luanne",age:25} | 0 | 5.0 | false | 1 | ==> +--------------------------------------------------------------------------------------+ ==> 3 rows

現(xiàn)在，讓我們進行一些過濾，以擺脫與亞當(dāng)已經(jīng)成為朋友的人–推薦這些人沒有多大意義！

MATCH (me:Person {name: "Adam"}) MATCH (me)-[:FRIEND_OF]-()-[:FRIEND_OF]-(potentialFriend)WITH me, potentialFriend, COUNT(*) AS friendsInCommonWITH me,potentialFriend,SIZE((potentialFriend)-[:LIVES_IN]->()<-[:LIVES_IN]-(me)) AS sameLocation,abs( me.age - potentialFriend.age) AS ageDifference,LABELS(me) = LABELS(potentialFriend) AS gender,friendsInCommonWHERE NOT (me)-[:FRIEND_OF]-(potentialFriend)RETURN me,potentialFriend,SIZE((potentialFriend)-[:LIVES_IN]->()<-[:LIVES_IN]-(me)) AS sameLocation,abs( me.age - potentialFriend.age) AS ageDifference,LABELS(me) = LABELS(potentialFriend) AS gender,friendsInCommon==> +---------------------------------------------------------------------------------------+ ==> | me | potentialFriend | sameLocation | ageDifference | gender | friendsInCommon | ==> +---------------------------------------------------------------------------------------+ ==> | Node[1007]{name:"Adam",age:30} | Node[1006]{name:"Vince",age:40} | 0 | 10.0 | true | 1 | ==> | Node[1007]{name:"Adam",age:30} | Node[1005]{name:"Daniela",age:20} | 0 | 10.0 | false | 1 | ==> | Node[1007]{name:"Adam",age:30} | Node[1008]{name:"Luanne",age:25} | 0 | 5.0 | false | 1 | ==> +---------------------------------------------------------------------------------------+ ==> 3 rows

在這種情況下，我們實際上并未將任何人過濾掉，但是對于其他一些人，我們會看到潛在朋友數(shù)量的減少。

我們的最后一步是為每個我們認(rèn)為對提出朋友建議很重要的功能評分。

如果人們居住在與亞當(dāng)相同的地方或性別相同，我們將給滿分10分，否則給0分。 對于ageDifference和friendsInCommon，我們將應(yīng)用對數(shù)曲線，以使這些值不會對我們的最終分?jǐn)?shù)產(chǎn)生不成比例的影響。 我們將使用ParetoScoreTransfomer中定義的公式來執(zhí)行此操作：

public <OUT> float transform(OUT item, float score) {if (score < minimumThreshold) {return 0;}double alpha = Math.log((double) 5) / eightyPercentLevel;double exp = Math.exp(-alpha * score);return new Double(maxScore * (1 - exp)).floatValue();}

現(xiàn)在，對于我們完整的推薦查詢：

MATCH (me:Person {name: "Adam"}) MATCH (me)-[:FRIEND_OF]-()-[:FRIEND_OF]-(potentialFriend)WITH me, potentialFriend, COUNT(*) AS friendsInCommonWITH me,potentialFriend,SIZE((potentialFriend)-[:LIVES_IN]->()<-[:LIVES_IN]-(me)) AS sameLocation,abs( me.age - potentialFriend.age) AS ageDifference,LABELS(me) = LABELS(potentialFriend) AS gender,friendsInCommonWHERE NOT (me)-[:FRIEND_OF]-(potentialFriend)WITH potentialFriend,// 100 -> maxScore, 10 -> eightyPercentLevel, friendsInCommon -> score (from the formula above)100 * (1 - exp((-1.0 * (log(5.0) / 10)) * friendsInCommon)) AS friendsInCommon,sameLocation * 10 AS sameLocation,-1 * (10 * (1 - exp((-1.0 * (log(5.0) / 20)) * ageDifference))) AS ageDifference,CASE WHEN gender THEN 10 ELSE 0 END as sameGenderRETURN potentialFriend,{friendsInCommon: friendsInCommon,sameLocation: sameLocation,ageDifference:ageDifference,sameGender: sameGender} AS parts,friendsInCommon + sameLocation + ageDifference + sameGender AS score ORDER BY score DESC==> +---------------------------------------------------------------------------------------+ ==> | potentialFriend | parts | score | ==> +---------------------------------------------------------------------------------------+ ==> | Node[1006]{name:"Vince",age:40} | {friendsInCommon -> 14.86600774792154, sameLocation -> 0, ageDifference -> -5.52786404500042, sameGender -> 10} | 19.33814370292112 | ==> | Node[1008]{name:"Luanne",age:25} | {friendsInCommon -> 14.86600774792154, sameLocation -> 0, ageDifference -> -3.312596950235779, sameGender -> 0} | 11.55341079768576 | ==> | Node[1005]{name:"Daniela",age:20} | {friendsInCommon -> 14.86600774792154, sameLocation -> 0, ageDifference -> -5.52786404500042, sameGender -> 0} | 9.33814370292112 | ==> +----------------------------------------------------------------------------------------+

最終查詢還不錯-唯一真正復(fù)雜的部分是對數(shù)曲線計算。 用戶定義的功能將在將來發(fā)揮作用。

這種方法的好處是我們不必走出密碼的道路，因此，如果您對Java不滿意，仍然可以進行實時建議！ 另一方面，推薦引擎的不同部分混合在一起，因此要查看整個管道并不像使用圖形感知框架那樣容易。

下一步是將其應(yīng)用于Twitter圖形，并在此提供關(guān)注者建議。

翻譯自: https://www.javacodegeeks.com/2015/03/neo4j-generating-real-time-recommendations-with-cypher.html

neo4j cypher

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