Is there a way to use non-text fields for document similarity in ElasticSearch, such as dates or integers?
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Using the more_like_this feature of ElasticSearch, I understand how to use tf-idf or other metrics to find similar documents based on text fields. However what if my use case is more like this:
# Name Description Price Date
--------------------------------------------
1 A B C Ba Bi Bou 100.0 12-01-18
--------------------------------------------
2 A B Z Ba Bi Xon 250.0 01-11-11
3 X Y Z Xa Xu Xon 100.0 12-02-18
Based on text fields only, document #2 should score higher than document #3 in terms of similarity with document #1, since document #3 would score 0. However, taking "price" and "date" into account, document #3's score should raise.
Can I do something like that with ElasticSearch? The only documentation I find deal with text fields. Adding non-text fields to the more_like_this query doesn't cause any exceptions, however scores are completely unaffected.
elasticsearch similarity asked Nov 12 at 9:54
user6377061
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up vote
1
down vote
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Using the more_like_this feature of ElasticSearch, I understand how to use tf-idf or other metrics to find similar documents based on text fields. However what if my use case is more like this:
# Name Description Price Date
--------------------------------------------
1 A B C Ba Bi Bou 100.0 12-01-18
--------------------------------------------
2 A B Z Ba Bi Xon 250.0 01-11-11
3 X Y Z Xa Xu Xon 100.0 12-02-18
Based on text fields only, document #2 should score higher than document #3 in terms of similarity with document #1, since document #3 would score 0. However, taking "price" and "date" into account, document #3's score should raise.
Can I do something like that with ElasticSearch? The only documentation I find deal with text fields. Adding non-text fields to the more_like_this query doesn't cause any exceptions, however scores are completely unaffected.
elasticsearch similarity asked Nov 12 at 9:54
user6377061
62
add a comment |
up vote
1
down vote
favorite
up vote
1
down vote
favorite
Using the more_like_this feature of ElasticSearch, I understand how to use tf-idf or other metrics to find similar documents based on text fields. However what if my use case is more like this:
# Name Description Price Date
--------------------------------------------
1 A B C Ba Bi Bou 100.0 12-01-18
--------------------------------------------
2 A B Z Ba Bi Xon 250.0 01-11-11
3 X Y Z Xa Xu Xon 100.0 12-02-18
Based on text fields only, document #2 should score higher than document #3 in terms of similarity with document #1, since document #3 would score 0. However, taking "price" and "date" into account, document #3's score should raise.
Can I do something like that with ElasticSearch? The only documentation I find deal with text fields. Adding non-text fields to the more_like_this query doesn't cause any exceptions, however scores are completely unaffected.
elasticsearch similarity asked Nov 12 at 9:54
user6377061
62
Using the more_like_this feature of ElasticSearch, I understand how to use tf-idf or other metrics to find similar documents based on text fields. However what if my use case is more like this:
# Name Description Price Date
--------------------------------------------
1 A B C Ba Bi Bou 100.0 12-01-18
--------------------------------------------
2 A B Z Ba Bi Xon 250.0 01-11-11
3 X Y Z Xa Xu Xon 100.0 12-02-18
Based on text fields only, document #2 should score higher than document #3 in terms of similarity with document #1, since document #3 would score 0. However, taking "price" and "date" into account, document #3's score should raise.
Can I do something like that with ElasticSearch? The only documentation I find deal with text fields. Adding non-text fields to the more_like_this query doesn't cause any exceptions, however scores are completely unaffected.
elasticsearch similarity elasticsearch similarity asked Nov 12 at 9:54
user6377061
62
asked Nov 12 at 9:54
user6377061
62
asked Nov 12 at 9:54
user6377061
62
asked Nov 12 at 9:54
user6377061
62
asked Nov 12 at 9:54
user6377061
62
62
add a comment |
add a comment |
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