CSCE 470 Lecture 31

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Learning to Rank

In the beginning, there was boolean retrieval: no ranking.

Ranked results:

• Jaccard (content-bsaed)
• TF-IDF and cosine (content-based)
• Link analysis: PageRank and HITS (not content-based)

Example:

Query: "twerk team"

Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \begin{align} score(q, d_1) &= 0.5 \cdot \cos(q,d_1) + 0.5 \cdot PR(d_1) score(q, d_2) &= 0.5 \cdot \cos(q,d_2) + 0.5 \cdot PR(d_2) \end{align}}

Other factors we might care about:

• beauty (aesthetics)
• recency of update
• clicks in past day
• bytes
• load time
• query in title

Each of these could have its own parameter:

Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle score(q, d_1) = \sum \alpha_i \, \mathrm{param}(q,d_1)}

We could use machine learning to figure out the best value of these parameters

Simple Example

Term proximity $\omega$ is minimum query window size: the query words occur across $\omega$ words.

• "penguin logo" with Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \omega = 4} could mean "penguin blah blah logo" appears on the page.
• Smaller window size is better

Example	Doc ID	Query	Cosine	Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \omega}	Judgement
Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \phi_1}	37	linux operating system	0.032	3	relevant
Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \phi_2}	37	penguin logo	0.02	4	nonrelevant
Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \phi_3}	238	operating system	0.043	2	relevant
Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \phi_4}	238	runtime environment	0.004	2	nonrelevant

We can create high-dimensional space over cosine and Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \omega} features!

Use hi-dimensional feature space, find relevant/nonrelevant classifier (need training data)

In 2008, Google admitted to using over 200 features. Now they're using well over 1000.

• is tilde in url? — FEATURE!
• how many times has the article been tweeted in the last day/month/year/etc.? — FEATURE!
• does it have a picture of a cat on the page? — FEATURE!

This is a high abstraction of boolean retrieval. High abstraction of ranked retrieval will be discussed next time.