That is a very complicated design. What are you trying to achieve? Maybe there is a different approach that is simpler.
wunder Walter Underwood wun...@wunderwood.org http://observer.wunderwood.org/ (my blog) > On Jul 7, 2016, at 9:26 AM, Mark T. Trembley <mark.tremb...@etrailer.com> > wrote: > > That works with static boosts based on documents matching the query "Boost2". > I want to apply a different boost to documents based on the value assigned to > Boost2 within the document. > > From my sample documents, when running a query with "Boost2," I want > Document2 boosted by 20.0 and Document6 boosted by 15.0: > > { > "id" : "Document2_Boost2", > "B1_s" : "Boost2", > "B1_f" : 20 > } > { > "id" : "Document6_Boost2", > "B1_s" : "Boost2", > "B1_f" : 15 > } > > > On 7/7/2016 10:21 AM, Walter Underwood wrote: >> This looks like a job for “bq”, the boost query parameter. I used this to >> boost textbooks which were used at the student’s school. bq does not force >> documents to be included in the result set. It does affect the ranking of >> the included documents. >> >> bq=B1_ss:Boost2 will boost documents that match that. You can use weights, >> like bq=B1_ss:Boost2^10 >> >> Here is the relationship between fq, q, and bq: >> >> fq: selection, does not affect ranking >> q: selection and ranking >> bq: does not affect selection, affects ranking >> >> wunder >> Walter Underwood >> wun...@wunderwood.org >> http://observer.wunderwood.org/ (my blog) >> >> >>> On Jul 7, 2016, at 7:30 AM, Mark T. Trembley <mark.tremb...@etrailer.com> >>> wrote: >>> >>> I have a question about the best way to rank my results based on a score >>> field that can have different values per document and where each document >>> can have different scores based on which term is queried. >>> >>> Essentially what I'm wanting to have happen is provide a list of terms that >>> when matched via a query it returns a corresponding score to help boost the >>> original document. So if I had a document with a multi-valued field named >>> B1_ss with terms [Boost1|10], [Boost2|20], [Boost3|100] and my search query >>> is "Boost2", I want that document's result to be boosted by 20. Also note >>> that "Boost2" can boost different documents at different levels. The query >>> to select the actual documents will select against other fields in the >>> document and could possibly return documents with any combination of B1 >>> terms. >>> >>> I'm still trying to figure out how best to model this in my index, either >>> as child documents, or in another collection, or if it would make more >>> sense to figure out how to make it work via payloads or by boosting the >>> terms at index time. >>> >>> I'm running Solr 5.5.1 in cloud mode. Each server has a complete replica of >>> all collections. >>> >>> The document structure I've been toying with the most is to put the boosts >>> into a separate index and join them using !join syntax and returning the >>> scores, but I've not had any luck getting quality results from those tests. >>> The extra "scores" index is structured like this (I'll add the json for my >>> test collections at the end of the email): >>> id:Document1_Boost1 >>> B1_s:Boost1 >>> B1_f:10 >>> id:Document1_Boost3 >>> B1_s:Boost3 >>> B1_f:100 >>> Using this structure, I get close, but the scores are not what I'm >>> expecting. If I use the following query, the explain says it's using the >>> score from Document6_Boost2 even though my query is specifying B1_s:Boost3 >>> http://localhost:8983/solr/generic/select?q={!join from=id to=B1_name_ss >>> fromIndex=scores score=max}B1_s:Boost3{!func}B1_f&fl=*,score&debugQuery=true >>> >>> <lstname="explain"> >>> <strname="Document6"> >>> *3.379996* = Score based on join value Document6_Boost2 >>> </str> >>> <strname="Document1"> >>> *2.2533307* = Score based on join value Document1_Boost1 >>> </str> >>> <strname="Document7"> >>> *0.24786638* = Score based on join value Document7_Boost333 >>> </str> >>> <strname="Document3">*0.0* = Score based on join value >>> Document3_NoBoost</str> >>> </lst> >>> >>> My guess is that it's now doing an all document query on the "scores" >>> collection to return the scores in addition to the B1_s query I've passed >>> in. I can't figure out where it's getting those scores from as a simple >>> query against the "scores" collection returns scores like I'd expect to see >>> them based on a similar query: >>> http://192.168.1.194:8983/solr/scores/select?q=B1_s:Boost3 AND >>> _val_:B1_f&fl=score,*&debugQuery=true >>> >>> <lstname="explain"> >>> <strname="Document1_Boost3"> >>> *46.834885* = sum of: 1.7682717 = weight(B1_s:Boost3 in 1) >>> [ClassicSimilarity], result of: 1.7682717 = score(doc=1,freq=1.0), product >>> of: 0.8926926 = queryWeight, product of: 1.9808292 = idf(docFreq=2, >>> maxDocs=8) 0.45066613 = queryNorm 1.9808292 = fieldWeight in 1, product of: >>> 1.0 = tf(freq=1.0), with freq of: 1.0 = termFreq=1.0 1.9808292 = >>> idf(docFreq=2, maxDocs=8) 1.0 = fieldNorm(doc=1) 45.066612 = >>> FunctionQuery(float(B1_f)), product of: 100.0 = float(B1_f)=100.0 1.0 = >>> boost 0.45066613 = queryNorm >>> </str> >>> <strname="Document6_Boost3"> >>> *15.288256* = sum of: 1.7682717 = weight(B1_s:Boost3 in 5) >>> [ClassicSimilarity], result of: 1.7682717 = score(doc=5,freq=1.0), product >>> of: 0.8926926 = queryWeight, product of: 1.9808292 = idf(docFreq=2, >>> maxDocs=8) 0.45066613 = queryNorm 1.9808292 = fieldWeight in 5, product of: >>> 1.0 = tf(freq=1.0), with freq of: 1.0 = termFreq=1.0 1.9808292 = >>> idf(docFreq=2, maxDocs=8) 1.0 = fieldNorm(doc=5) 13.519984 = >>> FunctionQuery(float(B1_f)), product of: 30.0 = float(B1_f)=30.0 1.0 = boost >>> 0.45066613 = queryNorm >>> </str> >>> </lst> >>> >>> I feel like I'm getting close to what I need, but it's just not clear to me >>> what I'm missing at this point. >>> >>> The other option I've been toying with is using payloads, but actually >>> utilizing the payloads as part of the scoring process is beyond me at this >>> time. >>> >>> Any thoughts or hints on the best way to boost the relevancy of these >>> scoreswould be appreciated. >>> Thanks >>> Mark >>> >>> >>> >>> >>> >>> >>> >>> GENERIC: >>> { >>> "id" : "Document1", >>> "B1_ss" : ["Boost1|10","Boost3|100"], >>> "title_s" : "Title1" >>> ,"otherstuff_ss" : ["stuff1","suggestion"] >>> ,"B1_name_ss" : ["Document1_Boost1","Document1_Boost3"] >>> }, >>> { >>> "id" : "Document2", >>> "B1_ss" : ["Boost2|20"], >>> "name_s" : "Product2", >>> "title_s" : "Title2" >>> ,"otherstuff_ss" : ["stuff2","recommendation"] >>> ,"B1_name_ss" : ["Document2_Boost1"] >>> }, >>> { >>> "id" : "Document3", >>> "name_s" : "Product3", >>> "B1_ss" : ["NoBoost"], >>> "title_s" : "Title3" >>> ,"otherstuff_ss" : ["stuff3","new","suggestion"] >>> ,"B1_name_ss" : ["Document3_NoBoost"] >>> }, >>> { >>> "id" : "Document4", >>> "name_s" : "Product4", >>> "title_s" : "Title4" >>> ,"otherstuff_ss" : ["stuff4","old","suggestion"] >>> } , >>> { >>> "id" : "Document5", >>> "name_s" : "Product5", >>> "title_s" : "Title5" >>> ,"otherstuff_ss" : ["stuff5","recommendation"] >>> }, >>> { >>> "id" : "Document6", >>> "name_s" : "Product6", >>> "B1_ss" : ["Boost2|15","Boost3|30"], >>> "title_s" : "Title6" >>> ,"B1_name_ss" : ["Document6_Boost2","Document6_Boost3"] >>> }, >>> { >>> "id" : "Document7", >>> "name_s" : "Product7", >>> "B1_ss" : ["NoBoost","Boost333|1.1"], >>> "title_s" : "Title7" >>> ,"B1_name_ss" : ["Document7_NoBoost","Document7_Boost333"] >>> } >>> >>> SCORES: >>> { >>> "id" : "Document1_Boost1", >>> "B1_s" : "Boost1", >>> "B1_f" : 10 >>> }, >>> { >>> "id" : "Document1_Boost3", >>> "B1_s" : "Boost3", >>> "B1_f" : 100 >>> }, >>> { >>> "id" : "Document2_Boost2", >>> "B1_s" : "Boost2", >>> "B1_f" : 20 >>> }, >>> { >>> "id" : "Document3_NoBoost", >>> "B1_s" : "NoBoost" >>> }, >>> { >>> "id" : "Document6_Boost2", >>> "B1_s" : "Boost2", >>> "B1_f" : 15 >>> }, >>> { >>> "id" : "Document6_Boost3", >>> "B1_s" : "Boost3", >>> "B1_f" : 30 >>> }, >>> { >>> "id" : "Document7_NoBoost", >>> "B1_s" : "NoBoost" >>> }, >>> { >>> "id" : "Document7_Boost333", >>> "B1_s" : "Boost333", >>> "B1_f" : 1.1 >>> } >>> >> >
