↳
Sorting the strings is not optimal because each sort is O(N log N) where N is the number of characters in each word. A more optimal solution is to create a function to encode each word as a hash table of character frequencies, which is O(N) for each word. Menos
↳
sort the strings and compare
↳
Use collaborate filtering to compare personal preference with others. If A and B are similar, we can recommend preferred items in B to A. Menos
↳
Why downvote on other answer? He/she is right. Collaborative filtering is the most common strategy for recommendation systems. You see user A buys these things and user B also bought those things but user B bought this other thing too so let's show that thing to User A. Menos
↳
I think you mean Normal distribution! If you are using R use set.seed(). You can then use rnorm() with size, mean & SD. e.g. >set.seed(123) >rnorm(100, 2, 5) Menos
↳
I'm the original poster, sorry for my typo. I actually mean multinomial distribution. And the advanced question was, if the probability is a skewed distribution, how would you speed up your algorithm. You can find both answer from Wikipedia. :) Menos
↳
Provided examples from my education and work.
↳
Comme une personne stable financièrement avec un plan familial en processus
↳
All of them I prepare ahead, anticipating examples for various job characteristic experiences I had in the past Menos
↳
I got the optimal solution (with a couple nudges but time to spare), yet apparently this was the only module where I did not "meet expectations." Shame that some presumably small mistake in my first hour was enough to discount the otherwise very strong 6 hour interview. Menos
↳
Used my background and current situation with my Fiance to answer. They were very happy with the interview. Menos