Collection Selection Overview Coll 1 Coll 3 Coll 2 document relevant to Query A document relevant to Query B document relevant to Query C User X Query A User Y User Z Query B Query C Query A Clones Broker BROKER ACTIONS 1) Receives query from user 2) Decides which collections are likely to contain resources relevant to the query, ranks them 3) Forwards query to top-n ranked collection servers 4) Receives results from collection servers 5) Merges and presents results to user Step 2 is the Collection Selection (a/k/a Database Selection) Problem. This is our focus.
TREC-Based Test Environment 6 Sources: AP, FR, PATN, SJM, WSJ, ZIFF –Sources partitioned into a total of 236 collections (a/k/a sites, databases) –Different decomposition (partitioning) methods 250 Queries –Often contain many [possibly repeating] query terms 1000s of Relevance Judgements –Humans listed documents relevant to each query –Complete only for queries 51-150
SYM Decomposition SYM = Source Year Month Example collections: –AP.88.02 - contains all Associated Press documents from February 1988 –ZIFF.90.12 - contains all ZIFF documents from December 1990 236 collections total
SYM’s “Goto AP” Problem In SYM, larger collections (# documents) tend to contain more of the relevant documents Some collections (AP) so large w.r.t. others that any algorithm favoring large collections over small collections will perform well, independently of the query contents
UDC Decomposition UDC = Uniform Document Count Example collections: –AP.01 - contains the first 1/236 (approx) of the complete set of documents; restricted to AP documents only [so no overlap w/ FR] –ZIFF.45 - contains the last 1/236 (approx) of the complete set documents 236 collections total
Baselines RBR = Relevance Based Ranking –merit query,coll = number of documents in collection which were deemed relevant to query Others...
Estimates Estimate names/categories: –gGLOSS - ideal(k) –CORI - U.Mass, best performance –SMART - ntn_ntn, etc. (tweaked components) –CVV - Yuwono/Lee’s “traditional” version –CVV p,q,r,s - hybrid, elements from CVV, SMART –SBR, random, etc. merit query,coll = estimated merit (“goodness”)
Ranks For each query, collections are ranked according to [estimated] merit (highest 1st) A ranking represents the order in which collections should be searched for docs Often want to consider only top-n collections –n is the “collection cut-off” or simply “cut-off”
Rank Comparisons An estimate’s ranks are compared against a baseline’s ranks (e.g., RBR vs CORI) Different comparison metrics: –Mean Squared Error (MSE), Spearman’s rho –P(n) = Precision at cut-off n prop. of estimated top-n collections with real merit > 0 –R(n) = Recall at cut-off n prop. of top-n real merit in estimated top-n collections –R^(n) = “R hat”(n) = H(n) prop. of total real merit in estimated top-n collections
Overall Performance Metric In this study, we use R(avg) averaged over all queries This is a normalized “area under the curve”
What is CVV? CVV = Cue Validity Variance CVV algorithm –a/k/a “CVV ranking method” –an estimator, generates merits (which can be ranked and compared against a baseline) –consists of CVV component, DF component CVV component –derived from DF and N What are DF and N?
Terminology, cont. C = set of collections in the system –|C| = number of collections in the system (236) N coll = number of documents in collection DF term, coll = “Document Frequency” –number of documents in collection in which term occurs at least once –So, DF term,coll <= N coll
Cue Validity (CV) Density term,coll = DF term,coll / N coll External term,coll = (DF term,c ) / (N c ) c != coll c != coll CV term,coll = Density term,coll Density term,coll + External term,coll CV term = (CV term,coll ) / |C| coll in C CV term,coll values are always between 0 and 1
Cue Validity Variance (CVV) CVV term = (CV term,coll - CV term ) 2 / |C| coll in C merit query,coll = estimated “Goodness” = (CVV term * DF term,coll ) term in query 2 components The basic CVV algorithm ignores the number of times a term occurs in the query (Query Term Weight)
Basic CVV “Problem” What if query = “cat cat dog”? –Basic CVV ignores the number of times “cat” appears in the query, so merits (and consequently ranks) will be the same as for query “cat dog” –Unlike in CVV’s development environment, many of our test queries have multiply-occurring terms –Performance of basic CVV tends to be noticeably poorer than that of algorithms which incorporate the query term frequency in their merit calculations –We can modify CVV to incorporate query term frequency
Enchancing CVV with QTW QTW query,term = query term weight (or freq) = # of times term occurs in query merit query,coll = (CVV term * DF term,coll * QTW query,term ) term in query 3 components
Effects of QTW Enhancement For R(avg) averaged over all queries… –SYM performance increased from.8416 to.8486 –UDC performance increased from.6735 to.6846 –Meanwhile, CORI’s SYM =.8972, UDC =.7884 –So, performance increased, but not dramatically For most individual queries, performance improved or stayed the same, but for 20-30% of queries, performance decreased –Could QTW be overcorrecting?
Further Questions... What if we varied the degree to which QTW influenced the merit calculations? We could: –Add an exponent to the QTW component: merit term,coll = (CVV term * DF term,coll * QTW term e ) –Evaluate using a range of exponents, searching for version that maximizes performance (e = 0, 0.5, 1, 2, …) What if we added exponents to the other two components as well: CVV and DF? What about adding an ICF component?
Inverse Collection Frequency (ICF) CF term = collection frequency of term = # of collections in which term occurs ICF term = log ((|C| + 1) / CF term ) ICF is used to decrease contribution of terms which appear in many collections and are therefore not good discriminators; terms which occur in few collections are best discriminators. ICF has shown useful in other algorithms.
Final Enhancement Equation merit query,coll = (CVV term p * DF term,coll q * QTW query,term r * ICF term s ) term in query 4 components Notes: –DF is the only component dependent upon collection –CVV(1,1,0,0) = Basic CVV = CVV(0) or “cvv” –CVV(1,1,1,0) = QTW-Enhanced CVV = CVV(1) –CVV(0,1,1,2) = ntn_ntn –CVV(*,0,*,*) = alphabetical (same merits for all collections)
P E R F O R M A N C E EstimateSYM UDC Joint CORI.8972.7884.8428 1.0 0.5 2.0 2.0.8938.7373.8155 1.0 0.5 2.0 0.7.8839.7776.8307 0.5 0.2 3.0 1.0.8937.7712.8325 Basic CVV.8416.6735.7576 Ideal(0).8570.7146.7858 ntn_ntn.8729.7356.8042 Note: ntn_ntn = 0.0 1.0 1.0 2.0
Essentiality What’s the best performance you can get if you hold a component’s exponent at 0? CVV component appears to be the least essential; DF appears to be the most essential Omitted Best Performer and Performance Comp SYM UDC Joint. none 1.0 0.5 2.0 2.0 (.8938) 1.0 0.5 2.0 0.7 (.7776) 0.5 0.2 3.0 1.0 (.8325) CVV 0.0 0.5 3.0 3.0 (.8932) 0.0 0.5 2.0 1.0 (.7737) 0.0 0.5 3.0 1.0 (.8298) DF tied (.6081) tied (.6017)tied (.6049) QTW 1.0 0.5 0.0 2.0 (.8851) 3.0 0.5 0.0 0.0 (.7525) 0.0 0.5 0.0 1.0 (.8053) ICF 2.0 0.5 3.0 0.0 (.8755) 3.0 0.5 2.0 0.0 (.7647) 3.0 0.5 3.0 0.0 (.8188)
Open Questions How much would other algorithms’ performances improve if we tweaked them? –Would CORI get so much better that a CVV hybrid couldn’t even get close? –Or, is CORI already optimized? Is there value in automating these searches, using adaptive programming?
Basic CVV Example Given: –query1 = “cat dog” –CVV “cat” = 0.8 (“cat” is unevenly distributed) –CVV “dog” = 0.2 (“dog” is more evenly distributed) –DF “cat”,collA = 1 (“cat” appears in only one of collA’s docs) –DF “dog”,collA = 20 –DF “cat”,collB = 5 –DF “dog”,collB = 3 merit query1,collA = (0.8*1) + (0.2*20) = 0.8 + 4 = 4.8 merit query1,collB = (0.8*5) + (0.2*3) = 4 + 0.6 = 4.6 For query1, collA would be ranked higher (better) than collB