what does k stand for?

[player1024]

what does k stand for? how do we decide it?

[richardwhiteii]

In the reciprocal_rank_fusion function, the parameter k is a constant that controls how much influence the rank of a document in individual search results has on the final fused score. The default value is set to 60.

The function computes the fused score for each document based on its rank in the individual search results generated by multiple queries. The score update formula is:

[ \text{fused_score} += \frac{1}{\text{rank} + k} ]

•   rank: The position of the document in the sorted list of results for a given query.
•   k: A constant that controls the influence of the rank.

The larger the value of k, the less impact a document’s rank in individual queries will have on its final fused score. Conversely, smaller values of k will result in higher sensitivity to the document’s individual ranks.

The choice of k depends on your specific application and may require tuning based on empirical results.

[Raudaschl]

This is it @player1024 and thank you @richardwhiteii for responding.

Great question about what "k" stands for in the reciprocal_rank_fusion function! You've touched on a key aspect of the function. As @richardwhiteii explained, "k" is a constant that basically helps you control how much weight the rank of each document carries in the final fused score.

The idea is that by tweaking the value of "k," you can fine-tune the balance between giving importance to documents that consistently appear high in individual query results and those that may only rank high in a few queries.

Think of "k" as a knob on a sound mixer. If you crank it up (higher value), you're sort of "smoothing out" the impact of individual rankings. Turn it down (lower value), and each ranking starts to really matter.

Example: Let's say we have two ranked lists for the query "machine learning papers":

List A: [Doc1, Doc2, Doc3] List B: [Doc3, Doc4, Doc5] With a lower "k" value (let's say k=1), Doc3, which appears high in both lists, would get a significantly higher combined score compared to the rest. But if we increase the "k" value to, say, 10, then the impact of the individual rankings will be somewhat "smoothed out," and documents like Doc1, Doc2, Doc4, and Doc5 might get a chance to get closer to Doc3 in the fused list.

Choosing the right "k" value can be a bit of an art and may require some experimentation. You might need to play around with different values to see which one produces the best results for your particular application.

Hope this helps clarify things! Let me know if you have any more questions.

Best, Adrian