上QQ阅读APP看书，第一时间看更新
3.3.8 FieldScoreQuery
FieldScoreQuery叫作函数查询(通过数字型的字段影响排序结果)，时间加权排序时会用到。
除了文本列上使用TF-IDF相似性的标准词查询之外，打分还参考数值列的相似性。相似性依赖于查询对象中的值和文档中的数值列中的值之间的距离。(例如，高斯函数，使用参数：m= [user input], s= 0.5)
例如，“猎头”使用搜索引擎找人。表示人的文档，有两列：
● description (文本列)；
● age (数值列)。
想要找这样的文档：
description:(x y z) age:30
但是age不是过滤条件，而是score的一部分(对于30岁的人，乘积因子是1.0；对于25岁的人，乘积因子是0.8；等等)。
把ValueSourceQuery和TermQuery包装在CustomScoreQuery中实现。
public class AgeAndContentScoreQueryTest extends TestCase{
public class AgeAndContentScoreQuery extends CustomScoreQuery {
protected float peakX;
protected float sigma;
//接收4个参数，其中subQuery表示文本列查询，valSrcQuery表示值查询
public AgeAndContentScoreQuery(Query subQuery, ValueSourceQuery
valSrcQuery, float peakX, float sigma) {
super(subQuery, valSrcQuery);
this.setStrict(true); // 不要归一化从ValueSourceQuery得到的分值！
this.peakX = peakX; // 哪个年纪的相关性最好
this.sigma = sigma;
}
@Override
public float customScore(int doc, float subQueryScore, float valSrcScore){
// subQueryScore来源于内容查询的td-idf分值
float contentScore = subQueryScore;
// valSrcScore是生日字段的值，表示成一个浮点数
// 把年纪值转换成高斯型年纪相关分值
float x = (2011- valSrcScore); // age
float ageScore = (float) Math.exp(-Math.pow(x - peakX, 2) / 2*sigma*sigma);
float finalScore = ageScore * contentScore;
System.out.println("#contentScore: " + contentScore);
System.out.println("#ageValue: " + (int)valSrcScore);
System.out.println("#ageScore: " + ageScore);
System.out.println("#finalScore: " + finalScore);
System.out.println("+++++++++++++++++");
return finalScore;
}
}
protected Directory directory;
protected Analyzer analyzer = new WhitespaceAnalyzer();
protected String fieldNameContent = "content";
protected String fieldNameDOB = "dob";
protected void setUp() throws Exception {
directory = new RAMDirectory();
analyzer = new WhitespaceAnalyzer();
//索引文档
String[] contents = {"foo baz1", "foo baz2 baz3", "baz4"};
int[] dobs = {1991, 1981, 1987}; // 生日
IndexWriter writer = new IndexWriter(directory, analyzer,
IndexWriter.MaxFieldLength.UNLIMITED);
for (int i = 0; i < contents.length; i++) {
Document doc = new Document();
doc.add(new Field(fieldNameContent, contents[i], Field.Store.YES,
Field.Index.ANALYZED)); // store并且index
doc.add(new NumericField(fieldNameDOB, Field.Store.YES,
true).setIntValue(dobs[i])); // store & index
writer.addDocument(doc);
}
writer.close();
}
public void testSearch() throws Exception {
String inputTextQuery = "foo bar";
float peak = 27.0f;
float sigma = 0.1f;
QueryParser parser = new QueryParser(fieldNameContent, analyzer);
Query contentQuery = parser.parse(inputTextQuery);
ValueSourceQuery dobQuery = new ValueSourceQuery( new
IntFieldSource(fieldNameDOB) );
// 或者FieldScoreQuery dobQuery = new
// FieldScoreQuery(fieldNameDOB, Type.INT);
CustomScoreQuery finalQuery = new AgeAndContentScoreQuery(contentQuery,
dobQuery, peak, sigma);
IndexSearcher searcher = new IndexSearcher(directory);
TopDocs docs = searcher.search(finalQuery, 10);
System.out.println("\n发现的文档：\n");
for(ScoreDoc match : docs.scoreDocs) {
Document d = searcher.doc(match.doc);
System.out.println("CONTENT: " + d.get(fieldNameContent) );
System.out.println("D.O.B.: " + d.get(fieldNameDOB) );
System.out.println("SCORE: " + match.score );
System.out.println("-----------------");
}
}
}
日期加权：
if (x >= now) {
score = now / x;
} else {
score = (float) (now / (now+sigma) - sigma / x);
}