# How to combine Elasticsearch function score query and text proximity scoring with weight? **URL:** \ **Category:** Elasticsearch\ **Created:** [January 10, 2019, 3:36am UTC](https://discuss.elastic.co/t/how-to-combine-elasticsearch-function-score-query-and-text-proximity-scoring-with-weight/163651 "2019-01-10T03:36:16Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![evalphobia](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/evalphobia/32/39612_2.png) [@evalphobia](https://discuss.elastic.co/u/evalphobia)\ **Post date:** [January 10, 2019, 3:36am UTC](https://discuss.elastic.co/t/how-to-combine-elasticsearch-function-score-query-and-text-proximity-scoring-with-weight/163651/1 "2019-01-10T03:36:16Z")
This question is originally from [StackOverflow's post](https://stackoverflow.com/questions/47135369/how-to-combine-elasticsearch-function-score-query-and-text-proximity-scoring). I've not been got any response, so I'm reposting here. * * * I'd like to use function score query and text proximity with weight. But the query does not correctly calculate score of "match\_phrase" in "query.function\_score.functions" For example, let's say I'm creating curation media and put a banner link of **"Financial articles in 2017"**. I'd like to filter and score like below, - Filter - Articles must be created in 2017. - The category must be **"finance"**. - Scoring - The more **"favorited"** article is, it gets the higher score. - If the article has the comment within the last 1 month, it gets the higher score. - If the article has certain tags, it gets the higher score. - (tags might be more than **100+** words) and data has precondition, - Precondition - the dataset is more than 2 million document - articles must have one "category" - articles might have one or more "tags" - tags might be more than **1000+** in the single article - "tags\_text" is string text and it is alphabetical order and joined by whitespace - ref: [Finding most similar arrays of integers in elasticsearch](https://stackoverflow.com/questions/24945500/finding-most-similar-arrays-of-integers-in-elasticsearch%5D) - "favorite" is number that people set the article to "favorite" (e.g. Facebook's Like button) ## example data and query ```auto // create index curl -XPUT 'http://localhost:9200/blog' ``` And put articles, ```auto // create articles curl -H "Content-Type: application/json" -XPUT http://localhost:9200/blog/article/1 -d ' { "article_id": 1, "title": "Fintech company list in London", "tags": ["fintech", "uk", "london"], "tags_text": "fintech london uk", "category": "finance", "created_at": "2016-12-01T00:00:00Z", "last_comment_at": null, "favorite": 100 }' curl -H "Content-Type: application/json" -XPUT http://localhost:9200/blog/article/2 -d ' { "article_id": 2, "title": "World economy", "tags": ["world", "economy", "regression", "war"], "tags_text": "economy regression war world", "category": "finance", "created_at": "2017-02-15T00:00:00Z", "last_comment_at": "2017-11-01T00:00:00Z", "favorite": 20 }' curl -H "Content-Type: application/json" -XPUT http://localhost:9200/blog/article/3 -d ' { "article_id": 3, "title": "Bitcoin bubble", "tags": ["bitcoin", "bubble", "btc", "mtgox", "wizsec"], "tags_text": "bitcoin btc bubble mtgox wizsec", "category": "finance", "created_at": "2017-08-03T00:00:00Z", "last_comment_at": null, "favorite": 50 }' curl -H "Content-Type: application/json" -XPUT http://localhost:9200/blog/article/4 -d ' { "article_id": 4, "title": "Virtual currency in China", "tags": ["bitcoin", "ico", "china"], "tags_text": "bitcoin china ico", "category": "finance", "created_at": "2017-09-03T00:00:00Z", "last_comment_at": null, "favorite": 10 }' curl -H "Content-Type: application/json" -XPUT http://localhost:9200/blog/article/5 -d ' { "article_id": 5, "title": "Average FX rate in 2017-10", "tags": ["fx", "currency", "doller"], "tags_text": "currency doller fx", "category": "finance", "created_at": "2017-11-01T00:00:00Z", "last_comment_at": null, "favorite": 10 }' curl -H "Content-Type: application/json" -XPUT http://localhost:9200/blog/article/6 -d ' { "article_id": 6, "title": "Cat and Dog", "tags": ["pet", "cat", "dog", "family"], "tags_text": "cat dog family pet", "category": "pet", "created_at": "2017-11-02T00:00:00Z", "last_comment_at": null, "favorite": 500 }' ``` Then execute query, _Note: Originaly "functions.filter.range.last\_comment\_at.from" is set to "now-30d". But to fix the changeable time and get unchanging results, asssume now is `2017-11-06` and use `2017-11-06||-30d`._ ```auto curl -H "Content-Type: application/json" -XGET 'http://localhost:9200/blog/article/_search' -d ' { "_source": { "includes": ["article_id", "title", "tags_text"] }, "query": { "function_score": { "functions": [ { "field_value_factor": { "factor": 1, "modifier": "log", "field": "favorite" }, "weight": 0.3 }, { "filter": { "range": { "last_comment_at": { "from": "2017-11-06||-30d", "to": null, "include_lower": true, "include_upper": false } } }, "weight": 0.3 }, { "filter": { "match_phrase": { "tags_text": { "query": "bitcoin fintech smartphone", "slop": 100 } } }, "weight": 0.4 } ], "query": { "bool": { "filter": [ {"term": {"category": "finance"} }, { "range": { "created_at": { "from": "2017-01-01T00:00:00", "to": "2017-12-31T23:59:59", "include_lower": true, "include_upper": true } } } ], "must": { "match_all": {} } } }, "score_mode": "sum" } } }' ``` The results are like below, ```auto { "hits": { "total": 4, "max_score": 0.69030905, "hits": [ { "_index": "blog", "_type": "article", "_id": "2", "_score": 0.69030905, "_source": { "article_id": 2, "tags_text": "economy regression war world", "title": "World economy" } }, { "_index": "blog", "_type": "article", "_id": "3", "_score": 0.509691, "_source": { "article_id": 3, "tags_text": "bitcoin btc bubble mtgox wizsec", "title": "Bitcoin bubble" } }, { "_index": "blog", "_type": "article", "_id": "5", "_score": 0.3, "_source": { "article_id": 5, "tags_text": "currency doller fx", "title": "Average FX rate in 2017-10" } }, { "_index": "blog", "_type": "article", "_id": "4", "_score": 0.3, "_source": { "article_id": 4, "tags_text": "bitcoin china ico", "title": "Virtual currency in China" } } ] } } ``` I checked result with "explain" but it seemed that "match\_phrase" query to "tags\_text" field does not affect to scoring at all. How to use weighted similarity scoring and function score query? (I checked by ES v2.4.0 and v6.3.0) Thanks, ---
**Author:** ![system](https://us1.discourse-cdn.com/elastic/original/3X/1/a/1ac57faf039f6b580b3f104ef42a2a89e41014de.png) [@system](https://discuss.elastic.co/u/system)\ **Post date:** [February 7, 2019, 3:36am UTC](https://discuss.elastic.co/t/how-to-combine-elasticsearch-function-score-query-and-text-proximity-scoring-with-weight/163651/2 "2019-02-07T03:36:16Z")
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