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This article mainly shows you the "sample analysis of elasticsearch scoring strategy", which is easy to understand and clear. I hope it can help you solve your doubts. Let me lead you to study and learn the article "sample Analysis of elasticsearch scoring Strategy".

Es search results, each result will have a "_ score" field, that is, find out the score of this result.

This involves the ranking of search results. Of course, the more relevant we want, the higher the result.

Es scoring strategy is designed to two points.

1: content relevance

For example, when Taobao searches for "Xiaomi TV set", Taobao's search will first divide the conditional word into "Xiaomi", "TV" and "TV" (it may also be more fine-grained), and then go to its own warehouse to find products that meet the conditions. of course, the top we see is the 32-inch millet TV, curved 4k some related televisions, but you jump to the fifth page or further back. You will find that the product is not Xiaomi TV set, there are some Xiaomi accessories, Xiaomi remote control board, or Hisense TV, Sony TV series and other products. This phenomenon means that the search will search the data in the database according to the participle of the input parameter, and the higher the matching degree of the field is, the higher the score of the result will be. (disclaimer: I don't know what Taobao's search engine is, just explain it with this phenomenon.)

2: map the weight value of the structure field

One of my own type fields is called zuName type: text type, and the word splitter is the ik word splitter used, with a weight value of 5.

Now if you have two fields: zuName weight 5 and zuDetail weight 3

In this way, if you search for multiple fields, the match between the two fields is the same, but the score returned by zuName is a little higher because the weight is set higher at the beginning. My current use is that when there are multiple type queries, the same field will be weighted differently so that the data of the two type can be distinguished.

The above is all the content of this article "sample Analysis of elasticsearch scoring Strategy". Thank you for reading! I believe we all have a certain understanding, hope to share the content to help you, if you want to learn more knowledge, welcome to follow the industry information channel!

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