<p>The probability ranking principle asserts that relevance has a probabilistic interpretation. According to this principle documents are ranked by a probability p(Rel|d, q), where Rel denotes the event of a document d being relevant to a query q. Robertson called this principle the probability ranking principle.</p>

CC BY-SA 3.0
2013-12-04T23:38:04.357Z
It would be better to use latex for math symbols and provide some reference.
Yixin Cao
CC BY-SA 3.0
2013-11-20T14:57:28.577Z
2013-11-20T14:57:28.577Z
ravi bhilavekar
<p>This is a clear definition of the <a href="http://nlp.stanford.edu/IR-book/html/htmledition/the-10-loss-case-1.html" rel="nofollow">probability ranking principle</a>.</p>

<blockquote>
If a reference retrieval system's response to each request is a ranking of the documents in the collection in order of decreasing probability of relevance to the user who submitted the request, where the probabilities are estimated as accurately as possible on the basis of whatever data have been made available to the system for this purpose, the overall effectiveness of the system to its user will be the best that is obtainable on the basis of those data.
</blockquote>

<p>As an IR professional (I work on Google Local Search), the principle is definitely wrong in the case that you assume the searcher doesn't examine every result and is just looking for one to satisfy their request.  Imagine you have 10 results that are excellent independently, but are near duplicates of each other.  Then the probability that the user finds something relevant barely increases as you spend results on the duplicate documents.  If you would have shown a diverse set of results with just one of the excellent, non-duplicate results, and others that satisfy the a secondary interpretation of the query, you'd do better to satisfy the user.</p>

<p>On the other hand, if your evaluation metrics for a set of results are just a sum of independent, result level metrics, then the principle is certainly correct.  You want to maximize the relevance of each result, and you can do that by picking the best ones in a vacuum.</p>

CC BY-SA 3.0
2012-09-22T15:36:25.597Z
2012-09-22T19:35:15.373Z
2012-09-22T19:35:15.373Z
rrenaud
<p>I am studying IR and </p>

<p>I am not clearly understand  "probability ranking principle" ( I tried to google the definition, but i couldn't find clear answer.)</p>

<p>I am assuming that it's system which response to each request is a ranking of the documents in the collection in order of decreasing probability.</p>

<p>but, I am not clearly understand concept of probability ranking principle</p>

<p>does anyone can explain it ?</p>

<p>thanks </p>

CC BY-SA 3.0
2012-09-22T12:26:49.003Z
What is IR? Also, can you gives us at least one of the definitions of "probability ranking system"?
Tyson Williams
CC BY-SA 3.0
2012-09-22T15:26:38.863Z
IR is information retrieval.
rrenaud
<pr.probability><ir.information-retrieval><search-engines>
question about probability ranking principle
Dc Redwing
# question about probability ranking principle

(Asked by: username_0 on 2012-09-22)

I am studying IR and

I am not clearly understand "probability ranking principle" ( I tried to google the definition, but i couldn't find clear answer.)

I am assuming that it's system which response to each request is a ranking of the documents in the collection in order of decreasing probability.

but, I am not clearly understand concept of probability ranking principle

does anyone can explain it ?

thanks

**Tags:** pr.probability, ir.information-retrieval, search-engines
> **Comments:**
> **username_1**: What is IR? Also, can you gives us at least one of the definitions of "probability ranking system"?  
> **username_2**: IR is information retrieval.  

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## 2 answers

### Answer by username_2 on 2012-09-22. Score: 7 | Accepted answer

This is a clear definition of the [probability ranking principle](http://nlp.stanford.edu/IR-book/html/htmledition/the-10-loss-case-1.html).

> If a reference retrieval system's response to each request is a ranking of the documents in the collection in order of decreasing probability of relevance to the user who submitted the request, where the probabilities are estimated as accurately as possible on the basis of whatever data have been made available to the system for this purpose, the overall effectiveness of the system to its user will be the best that is obtainable on the basis of those data.

As an IR professional (I work on Google Local Search), the principle is definitely wrong in the case that you assume the searcher doesn't examine every result and is just looking for one to satisfy their request. Imagine you have 10 results that are excellent independently, but are near duplicates of each other. Then the probability that the user finds something relevant barely increases as you spend results on the duplicate documents. If you would have shown a diverse set of results with just one of the excellent, non-duplicate results, and others that satisfy the a secondary interpretation of the query, you'd do better to satisfy the user.

On the other hand, if your evaluation metrics for a set of results are just a sum of independent, result level metrics, then the principle is certainly correct. You want to maximize the relevance of each result, and you can do that by picking the best ones in a vacuum.


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### Answer by username_3 on 2013-11-20. Score: 0

The probability ranking principle asserts that relevance has a probabilistic interpretation. According to this principle documents are ranked by a probability p(Rel|d, q), where Rel denotes the event of a document d being relevant to a query q. Robertson called this principle the probability ranking principle.

> **Comments:**
> **username_4**: It would be better to use latex for math symbols and provide some reference.  

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