Lucene关于实现Similarity自定义排序-白红宇

Lucene关于实现Similarity自定义排序

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发布时间：2019-06-29

本文共 11364 字，大约阅读时间需要 37 分钟。

开场白:

作为一个人才网站的搜索功能,不但需要考滤搜索性能与效率，与需要注意用户体验,主要体现于用户对搜索结果的满意程度.大家都知道Lucene的排序中,如果单纯使用Lucene的DefaultSimilarity作为一个相似度的排序,意思是说总体上越相关的记录需要排得越前,但事与愿违.这样使用户体现也表现得相当糟糕.关键字"程序员"标题中也不能保证全部都匹配到(搜索结果来自 www.jobui.com 职友集) [下图]

起因：之很长一段时间我都注重于搜索性能与速度的提高,而对于搜索结果对用户的体验却一直没有太多的关注,现在需要关注一下用户体现这个东西了.同时技术上也作为一些调整.具体表现如下.

1,用户最需要的搜索结果是标题命中.

2,因为我们从事人才招聘行业,所以职位的发布时间需要最新的.

所以经过各部门商量,职位搜索的结果排序应该是,相关度优先,然后才是职位的发布时间倒序.即如果关键字匹配是一定要全部命中了才会排在第一位,然后再是只命中一部分关键字记录.具体如下图,(搜索"php 开发",这样的话,只有php,开发这两个关键字都全部匹配了才会排前.然后全部命中关键字的记录按职位的发布时间来递减.)

开始:主要是继承Lucene中的Similarity作为一个相似度的实现,这里简单介绍一下相关的介绍

主要是几个排序影响因素去想的

在看代码之前先看看我们Lucene排序的一些影响因为,大家可以在搜索的时候，开启Explain的选项,这样就能看得清楚了

比如说,我现在要搜索 "开发工程" 这些关键字,然后就会把每一个Document的得分情况都列出来,大家就知道了,同时大家有没发现,这一个详细情况跟Similarity的需要实现的方法的因素基本都是对应的..比如 idf,tf queryNorm等方法..这样大家就有一个可以参考分析的方法了.

200.0 = (MATCH) sum of:   100.0 = (MATCH) weight(Name:开发^100.0 in 5), product of:     100.0 = queryWeight(Name:开发^100.0), product of:       100.0 = boost       1.0 = idf(docFreq=4, maxDocs=6)       1.0 = queryNorm     1.0 = (MATCH) fieldWeight(Name:开发 in 5), product of:       1.0 = tf(termFreq(Name:开发)=0)       1.0 = idf(docFreq=4, maxDocs=6)       1.0 = fieldNorm(field=Name, doc=5)   100.0 = (MATCH) weight(Name:工程^100.0 in 5), product of:     100.0 = queryWeight(Name:工程^100.0), product of:       100.0 = boost       1.0 = idf(docFreq=2, maxDocs=6)       1.0 = queryNorm     1.0 = (MATCH) fieldWeight(Name:工程 in 5), product of:       1.0 = tf(termFreq(Name:工程)=1)       1.0 = idf(docFreq=2, maxDocs=6)       1.0 = fieldNorm(field=Name, doc=5)   0.0 = (MATCH) weight(Info:开发^0.0 in 5), product of:     0.0 = queryWeight(Info:开发^0.0), product of:       0.0 = boost       1.0 = idf(docFreq=4, maxDocs=6)       1.0 = queryNorm     1.0 = (MATCH) fieldWeight(Info:开发 in 5), product of:       1.0 = tf(termFreq(Info:开发)=2)       1.0 = idf(docFreq=4, maxDocs=6)       1.0 = fieldNorm(field=Info, doc=5)   0.0 = (MATCH) weight(Info:工程^0.0 in 5), product of:     0.0 = queryWeight(Info:工程^0.0), product of:       0.0 = boost       1.0 = idf(docFreq=0, maxDocs=6)       1.0 = queryNorm     1.0 = (MATCH) fieldWeight(Info:工程 in 5), product of:       1.0 = tf(termFreq(Info:工程)=0)       1.0 = idf(docFreq=0, maxDocs=6)       1.0 = fieldNorm(field=Info, doc=5)

现在先看看实现 Similarity 类的方法

1 package com.kernaling;   2      3 import org.apache.lucene.index.FieldInvertState;   4      5 public class BaicaiPositionSimilarity extends Similarity {   6      7       /** Implemented as  8        *  state.getBoost()*lengthNorm(numTerms), where  9        *  numTerms is {
    @link FieldInvertState#getLength()} if {
    @link 10        *  #setDiscountOverlaps} is false, else it's {
    @link 11        *  FieldInvertState#getLength()} - {
    @link 12        *  FieldInvertState#getNumOverlap()}. 13        * 14        *  WARNING: This API is new and experimental, and may suddenly 15        *  change.
 */  16       @Override  17       public float computeNorm(String field, FieldInvertState state) {  18         final int numTerms;  19         if (discountOverlaps)  20           numTerms = state.getLength() - state.getNumOverlap();  21         else  22           numTerms = state.getLength();  23         return (state.getBoost() * lengthNorm(field, numTerms));  24       }  25           26       /** Implemented as 1/sqrt(numTerms). */  27       @Override  28       public float lengthNorm(String fieldName, int numTerms) {  29 //        System.out.println("fieldName:" + fieldName + "\tnumTerms:" + numTerms);  30 //      return (float)(1.0 / Math.sqrt(numTerms));  31           return 1.0f;  32       }  33           34       /** Implemented as 1/sqrt(sumOfSquaredWeights). */  35       @Override  36       public float queryNorm(float sumOfSquaredWeights) {  37 //      return (float)(1.0 / Math.sqrt(sumOfSquaredWeights));\  38         return 1.0f;  39       }  40     41       /** Implemented as sqrt(freq). */  42 //        term freq 表示 term 在一个document的出现次数,这里设置为1.0f表示不考滤这个因素影响  43 //    @Override  44 //    public float tf(float freq) {  45         return 1.0f;  46     47       }  48             49       /** Implemented as 1 / (distance + 1). */  50          //这里表示匹配的 term　与 term之间的距离因素,同样也不应该受影响  51       @Override  52       public float sloppyFreq(int distance) {  53         return 1.0f;  54       }  55             56       /** Implemented as log(numDocs/(docFreq+1)) + 1. */  57           //这里表示匹配的docuemnt在全部document的影响因素,同理也不考滤  58       @Override  59       public float idf(int docFreq, int numDocs) {  60         return 1.0f;  61       }  62             63       /** Implemented as overlap / maxOverlap. */  64           //这里表示每一个Document中所有匹配的关键字与当前关键字的匹配比例因素影响,同理也不考滤.  65       @Override  66       public float coord(int overlap, int maxOverlap) {  67         return 1.0f;  68       }  69     70       // Default false  71       protected boolean discountOverlaps;  72     73       /** Determines whether overlap tokens (Tokens with 74        *  0 position increment) are ignored when computing 75        *  norm.  By default this is false, meaning overlap 76        *  tokens are counted just like non-overlap tokens. 77        * 78        *  WARNING: This API is new and experimental, and may suddenly 79        *  change.
 80        * 81        *  @see #computeNorm 82 */  83       public void setDiscountOverlaps(boolean v) {  84         discountOverlaps = v;  85       }  86     87       /**@see #setDiscountOverlaps */  88       public boolean getDiscountOverlaps() {  89         return discountOverlaps;  90       }  91 }

按上面的相似度因素影响,基本上都设置为不受其他影响了,现在只剩下了关键字匹配数据的影响了,也就是我们需求中需要的.

然后做一个测试类:

1 package com.kernaling;    2       3 import java.io.File;    4 import java.io.StringReader;    5       6 import org.apache.lucene.document.Document;    7 import org.apache.lucene.document.Field;    8 import org.apache.lucene.index.IndexWriter;    9 import org.apache.lucene.index.Term;   10 import org.apache.lucene.index.IndexWriter.MaxFieldLength;   11 import org.apache.lucene.search.BooleanClause;   12 import org.apache.lucene.search.BooleanQuery;   13 import org.apache.lucene.search.Explanation;   14 import org.apache.lucene.search.IndexSearcher;   15 import org.apache.lucene.search.ScoreDoc;   16 import org.apache.lucene.search.Sort;   17 import org.apache.lucene.search.SortField;   18 import org.apache.lucene.search.TermQuery;   19 import org.apache.lucene.search.TopDocs;   20 import org.apache.lucene.search.TopFieldCollector;   21 import org.apache.lucene.store.NIOFSDirectory;   22 import org.wltea.analyzer.IKSegmentation;   23 import org.wltea.analyzer.Lexeme;   24 import org.wltea.analyzer.lucene.IKAnalyzer;   25      26 public class LuceneSortSample {   27     public static void main(String[] args) {   28         try{   29      30             String path = "./Index";   31             IKAnalyzer analyzer = new IKAnalyzer();   32             MySimilarity similarity = new MySimilarity();   33                  34             boolean isIndex = false;    // true:要索引,false:表示要搜索    35                  36             if(isIndex){   37                 IndexWriter writer = new IndexWriter(new NIOFSDirectory(new File(path)),analyzer,MaxFieldLength.LIMITED);   38                 writer.setSimilarity(similarity);   //设置相关度   39                      40                 Document doc_0 = new Document();   41                 doc_0.add(new Field("Name","java 开发人员", Field.Store.YES, Field.Index.ANALYZED));   42                 doc_0.add(new Field("Info","招聘 网站开发人员,要求一年或以上工作经验", Field.Store.YES, Field.Index.ANALYZED));   43                 doc_0.add(new Field("Time","20100201", Field.Store.YES, Field.Index.NOT_ANALYZED));   44                 writer.addDocument(doc_0);   45                      46                      47                 Document doc_1 = new Document();   48                 doc_1.add(new Field("Name","高级开发人员(java 方向)", Field.Store.YES, Field.Index.ANALYZED));   49                 doc_1.add(new Field("Info","需要有四年或者以上的工作经验,有大型项目实践,java基本扎实", Field.Store.YES, Field.Index.ANALYZED));   50                 doc_1.add(new Field("Time","20100131", Field.Store.YES, Field.Index.NOT_ANALYZED));   51                 writer.addDocument(doc_1);   52                      53                      54                 Document doc_2 = new Document();   55                 doc_2.add(new Field("Name","php 开发工程师", Field.Store.YES, Field.Index.ANALYZED));   56                 doc_2.add(new Field("Info","主要是维护公司的网站php开发,能独立完成网站的功能", Field.Store.YES, Field.Index.ANALYZED));   57                 doc_2.add(new Field("Time","20100201", Field.Store.YES, Field.Index.NOT_ANALYZED));   58                 writer.addDocument(doc_2);   59                      60                      61                 Document doc_3 = new Document();   62                 doc_3.add(new Field("Name","linux 管理员", Field.Store.YES, Field.Index.ANALYZED));   63                 doc_3.add(new Field("Info","管理及维护公司的linux服务器,职责包括完成mysql数据备份及日常管理,apache的性能调优等", Field.Store.YES, Field.Index.ANALYZED));   64                 doc_3.add(new Field("Time","20100201", Field.Store.YES, Field.Index.NOT_ANALYZED));   65                 writer.addDocument(doc_3);   66                      67                      68                 Document doc_4 = new Document();   69                 doc_4.add(new Field("Name","lucene开发工作师", Field.Store.YES, Field.Index.ANALYZED));   70                 doc_4.add(new Field("Info","需要两年或者以上的从事lucene java 开发工作的经验,需要对算法,排序规则等有相关经验,java水平及基础要扎实", Field.Store.YES, Field.Index.ANALYZED));   71                 doc_4.add(new Field("Time","20100131", Field.Store.YES, Field.Index.NOT_ANALYZED));   72                 writer.addDocument(doc_4);   73                      74                      75                 Document doc_5 = new Document();   76                 doc_5.add(new Field("Name","php 软件工程师", Field.Store.YES, Field.Index.ANALYZED));   77                 doc_5.add(new Field("Info","具有大量的php开发经验,如熟悉 java 开发,数据库管理则更佳", Field.Store.YES, Field.Index.ANALYZED));   78                 doc_5.add(new Field("Time","20100130", Field.Store.YES, Field.Index.NOT_ANALYZED));   79                 writer.addDocument(doc_5);   80                      81                 writer.close();   82                 System.out.println("数据索引完成");   83             }else{   84                 IndexSearcher search = new IndexSearcher(new NIOFSDirectory(new File(path)));   85                 search.setSimilarity(similarity);   86                 String keyWords = "java开发";   87                      88                      89                 String fiels[] = {"Name","Info"};   90                      91                 BooleanQuery bq = new BooleanQuery();   92                 for(int i=0;i

建立完索引后然后就可以直接搜索了.效果图如下:

可以看到,我们现在搜索关键字"开发工程", 然后就可以看到DocID:为 0,2为关键字全部命中的文档,然后这两个文档就按时间倒序排了.

然后,DocId 1,4,5的话,就只匹配到部分的关键字,它肯定会比全部命中关键字的记录要排序要后,然后中命中部分关键字的记录又会按发布时间来倒序排了一次

对了，我是用 Lucene3.0 作为开发包的.与Lucene2.XX的很多接口都改了,包括Similarity 的继承类的方法也不同, 所以大家要注思,不过经过测试,只要相同的实现那么效果也是一样的.

注意:从上边的测试结果可以看到一个疑问,这些记录匹配的关键字 开发工程　中,无论是命中全部关键字还是一个,得到的score都是一样的,但是排序的时候却按我们之前设置的意义去排序,理论上来说,只匹配一半的关键字,score会是全部匹配的一半的,这里的话,不知道是否是一个bug.有待继续研究.同时职友集www.jobui.com与百才招聘 www.baicai.com 这两个网站的搜索功能还没有把这个想法用到上边去,现在只在本地的测试服务器中有效,因为这段时间有其他事情要做.请大家见谅.过年后左右,大家会有一个全新的搜索体验..谢谢.

摘自：http://kernaling-wong.iteye.com/blog/586043

转载于:https://www.cnblogs.com/ibook360/archive/2011/11/14/2248124.html

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