You understand that you are making your site extremely easy to spam, right? This is how Microsoft became the top hit for “evil empire” on Google.
wunder Walter Underwood wun...@wunderwood.org http://observer.wunderwood.org/ (my blog) > On Jul 7, 2016, at 11:25 AM, Mark T. Trembley <mark.tremb...@etrailer.com> > wrote: > > I've found that it is definitely complicated! > > Essentially what I am attempting to do is boost products based on the number > of times that particular product has been selected via historical searches > using the same search term or phrase. > > > On 7/7/2016 11:55 AM, Walter Underwood wrote: >> 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 >>>>> } >>>>> >> >
