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Boosting & Biasing

Classical IR algorithms aren't the end all be all

• TF/IDF doesn't know your domain
• TF/IDF doesn't know your users

Examples For Today

• Data Domain: Tech Products
• Popularity
• Release date
• Price & margin
• Product type or category
• Users

In the Dark Ages

• Search & Score
• Filter (With Retry)
• (Default) Sort

Battle axe medieval created by Stefanvon Halenbach, available under Public Domain.

Trivial Example

q = the user input

vs.

q = +(the user input) suck:false^0.2

Real World Example (Ancient)

q = +(the user input)^100 (*:* -cat:(7, 9, 23))^5 rating:[4 TO *]^3 rating:[7 TO *]^10 popularity:[* TO 1000]^5

Real World Example (Today)

qq = the user input q = {!boost b=\$b v=\$qq} b = div(\$good,price) good = mul(rating,popularity,cat_weight))

Prerequisites

• Analyze User Behavior
• Determine User Preferences
• Quantify Strength of Preferences

Simple Example: Sam

• Tends to buy cheap products
• Recently looking at a lot of laptops
q = {!boost b=\$b v=\$qq} b = mul(query(\$pref),pow(\$diff,\$diffs)) pref = cat:laptop diff = div(1,price) diffs = 0.72

Simple Example: Sally

• Tends to buy new products
• Recently looking at "Apple" products
q = {!boost b=\$b v=\$qq} b = mul(query(\$pref),pow(\$diff,\$diffs)) pref = mfg:Apple diff = recip(ms(NOW,proddate),3.16e-11,1,1) diffs = 0.72

Cool Idea: Sweet Spots

Thanks to Code Cogs for the equation SVG file (source)

Graph generated using this gnuplot file.

Sweet Spot Example

Recently clicked on \$1000-1200 price facet

q = {!boost b=\$b v=\$qq} b = div(1,sqrt(sum(1,\$mult))) mult = mul(\$s,sub(sum(abs(sub(\$bias,\$min)), abs(sub(\$bias,\$max))), sub(\$max,\$min))) bias = price min = 1000 max = 1200 s = 0.08