gistfile1.txt

bf=linear(editorial_rating_i,20,-60)
bf=product(popularity_norm_d,2)
bf=sub(product(product(div(ratings_count_i,sum(10,ratings_count_i)),user_rating_i),14),30)
boost=freshness_boost
boost=pow(frequency_boost, 0.0)
bq=((node_id:783 AND NOT(editorial_rating_i:[* TO *]))^10)
bq=((type:event OR type:event_master OR (type:film AND release_date_tdt:[NOW/DAY-60DAY TO NOW/DAY+7DAY])) AND occurrence_dates:[NOW/DAY TO NOW/DAY+7DAY])^10 OR type:venue^5
bq=(node_id:1082 AND NOT(node_id:783 OR editorial_rating_i:[* TO *]))^8
bq=(sponsored_b:true AND (editorial_rating_i:4 OR (node_id:783 AND NOT editorial_rating_i:5)))^0.3333333333333333
bq=(sponsored_b:true AND (editorial_rating_i:[1 TO 2]))^0.1
bq=(sponsored_b:true AND NOT(editorial_rating_i:[* TO *] OR node_id:783))^0.1
bq=(sponsored_b:true AND editorial_rating_i:3)^0.16666666666666666
bq=(sponsored_b:true AND editorial_rating_i:5)^0.5
bq=node_id:3411^4
bq=node_id:4695^5
bq=node_id:784^4
bq=node_id:785^5
bq=node_id:786^4
bq=type:post^1.5
defType=edismax
fq=site_tag_s:uk-london OR site_tag_s:""
fq=type:event OR type:film OR type:venue OR type:page OR type:post
pf=announcement_s^15
pf=primary_tag_representation^50
pf=search_names^25
q.alt=*:*
qf=announcement_s^10
qf=indexed_annotation^4
qf=owner_annotation_s^2
qf=primary_tag_representation^60
qf=search_names^35
qf=tag_name^70
qs=50
rows=5
sort=score desc,editorial_rating_i desc,critics_choice_i desc,recommended_i asc,bookable_i desc
start=0
tie=0.1