Lucene6-如何用数值影响排名？

Question

    IndexWriter writer = new IndexWriter(FSDirectory.open(Paths.get(INDEX_LOCATION)),
        new IndexWriterConfig(new StandardAnalyzer()));
    writer.deleteAll();
    for (Contributor contributor : contributors) {
        Document doc = new Document();
        doc.add(new TextField("name", contributor.getName(), Field.Store.YES));
        doc.add(new StoredField("contribId", contributor.getContribId()));
        doc.add(new NumericDocValuesField("sum", sum));
        writer.addDocument(doc);
    }
    writer.close();

名称是我们要搜索的字段，总和是我们要对搜索结果进行加权的字段（但仍要考虑与作者名称的最佳匹配）。我不确定在这种情况下，将总和添加到文档中是否是正确的做法。我知道需要一些实验来找出如何最好地混合这两个因素的权重，但我的问题是我不知道如何首先做到这一点。

我能找到的任何例子要么是Lucene4之前的，要么似乎不起作用。我以为这就是我要找的，但似乎不起作用。感谢您的帮助！

Answer 1

如您链接的博客文章所示，您可以使用CustomScoRequery；这会给你很大的灵活性和对得分过程的影响，但这也有点矫枉过正。另一种可能是使用functionscorequery；由于它们的行为不同，我将对两者进行解释。

functionscorequery可以根据字段修改分数。

假设您创建了这样的搜索：

Query q = .... // pass the user input to the QueryParser or similar
TopDocs hits = searcher.search(query, 10); // Get 10 results

Query q = .....

// Note that a Float field would work better.
DoubleValuesSource boostByField = DoubleValuesSource.fromLongField("sum");

// Create a query, based on the old query and the boost
FunctionScoreQuery modifiedQuery = new FunctionScoreQuery(q, boostByField);

// Search as usual
TopDocs hits = searcher.search(query, 10);

doc.add(new StoredField("sum", sum)); 

然后我们必须创建自己的查询类：

private static class MyScoreQuery extends CustomScoreQuery {
    public MyScoreQuery(Query subQuery) {
        super(subQuery);
    }

    // The CustomScoreProvider is what actually alters the score
    private class MyScoreProvider extends CustomScoreProvider {

        private LeafReader reader;
        private Set<String> fieldsToLoad;

        public MyScoreProvider(LeafReaderContext context) {
            super(context);
            reader = context.reader();

            // We create a HashSet which contains the name of the field
            // which we need. This allows us to retrieve the document 
            // with only this field loaded, which is a lot faster.
            fieldsToLoad = new HashSet<>();
            fieldsToLoad.add("sum");
        }

        @Override
        public float customScore(int doc_id, float currentScore, float valSrcScore) throws IOException {
            // Get the result document from the index
            Document doc = reader.document(doc_id, fieldsToLoad);

            // Get boost value from index               
            IndexableField field = doc.getField("sum");
            Number number = field.numericValue();

            // This is just an example on how to alter the current score
            // based on the value of "sum". You will have to experiment
            // here.
            float influence = 0.01f;
            float boost = number.floatValue() * influence;

            // Return the new score for this result, based on the 
            // original lucene score.
            return currentScore + boost;
        }           
    }

    // Make sure that our CustomScoreProvider is being used.
    @Override
    public CustomScoreProvider getCustomScoreProvider(LeafReaderContext context) {
        return new MyScoreProvider(context);
    }       
}

现在可以使用新的查询类修改现有查询，类似于FunctionScoRequery:

Query q = .....

// Create a query, based on the old query and the boost
MyScoreQuery modifiedQuery = new MyScoreQuery(q);

// Search as usual
TopDocs hits = searcher.search(query, 10);

使用CustomScoRequery，可以以各种方式影响评分过程。但是请记住，每个搜索结果都调用CustomScore方法，所以不要在那里执行任何昂贵的计算，因为这会严重减慢搜索过程。