In an interview I was asked this question, You are the college principal and want to send a birthday wish to all the students who have their birthdays today. How do you implement this?
I said I will go with a Multi Hashset of dates where chaining is used so that each date has multiple studentIDs.(Each date will map to multiple students). But is there anything better that can be done?
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I have to write a paper about a project I have done, which is basically a booking app that recommends hotels based on the chosen location, the check-in and check-out dates and number of people.
Since I have to write about some theoretical concepts that I used, does anyone know what type of recommendation is it?
I've done some research about bayesian personalized ranking and item recommendation, but to be honest I don't know if that fits. I would be very thankful if you could give me any ideas.
I've noticed on food delivery apps it says something like 'orders with nearby collection and drop off points are grouped together for efficiency'. I have a similar problem where delivery jobs can come in real time or pre booked, and the algorithm needs to group jobs to get them done faster. I have data on distance between locations and how jobs are grouped manually.
I was wondering what kind of algorithms these big companies use (here its grab, foodpanda, deliveroo etc) to group orders. Is it like a secret?
Also, I was told this algorithm has to have AI in it because its a buzzword that clients love. I'm scratching my head trying to figure out how to incorporate that. e.g. use supervised learning and treat it like a classification problem on which person to choose for each job, based on distance or something?? The 'label' would be data on how humans grouped jobs, which isn't really optimal and the client wants an improvement from that as well.
My question is if these commercial algorithms out there for grouping food orders use AI, if its appropriate to use AI and how, and in general any insight into what kind of algorithms they use. Thanks in advance.
I need help to find the best algorithm/decision model (sorry I don't know how to call that precisely) to solve the following problem.
I have students living in a town, multiple schools in the town, and I need to assign each student to a school so that we achieve the "best combination of criteria".
The criteria are for example:
a diversity (boys/girls) close to the diversity of the group (i.e. not all the boys together)
distance to the school
age of the student
and there could be more criteria in the future
What I need is not the solution, that's why for example the exact list of criteria does not matter yet.
What I need is more an advice on what are the possible ways to solve this problem.
The "only" way I can think of at the moment is to write an algorithm to try all the possible combinations of students & schools and somehow calculate a score of each combination (each criteria would have a "weight") and then pick the solutions with the best scores. But with this approach, the amount of combination can be quite huge if we take for example 1000 students and 5 schools. So maybe there are other ways to do it.
Programming language is not important at the moment.
Thanks in advance for any help you can provide :)
This is a linear optimization problem, usually solved with the Simplex algorithm: https://en.wikipedia.org/wiki/Simplex_algorithm
I have a database of users with one field describing their interests in form of an array of strings. I'd like to write an algorithm which is able to take as input the list of users and returns groups of users with the same interests. The end goal would be to suggest the users what they may be interested into based on what users with similar interests are interested into. Could anybody suggest an algorithm that I could implement to achieve such result?
Thank you.
It's not very easy to describe my problem in one sentence (title). I want to find a person's interests by asking them some questions in order to assign to him attributes.
For exemple: In 10 questions (Do you love technology? Are you interested on economics? Are you more food than reading ?), I want to be able to find people's interests (Technology, Books reading, economics, ...) in order to give him attributes like technology, literature, politics, .... I also want that my program learn attributes from users answers.
I am looking for an algorithm which could help me in assigning attributes. For me, it is not a simple binary search (20 questions AI or similar) algorithm but a cluster-like AI.
Do you have any advice on this subject ?
First, classification is supervised learning while clustering is unsupervised. You can think in supervised learning like this:
I have all this groups already classified and I have a new individual/set of individuals, which group is the most suited for the individual?
As you train your model (eg: by hand like marking an email as spam) your individuals are most likely to be classified correctly.
The equivalent problem but in unsupervised learning is called clustering, you have a dataset, you have no model to support on and you want to get an idea (this is best suited for exploratory analysis) on hoy your data is grouped based on some metrics (variance, mean distance between each individual on the same group, so on so forth).
Have you tried using association rule based learning?