Can Julia be effectively used to implement a recommendation system for an Airbnb clone using user behavior and booking history, and which Julia packages are best suited for this?

Specific Domains Machine Learning
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We are developing an Airbnb clone script and exploring Julia for building a recommendation system based on user behavior and booking history. Your primary goal is to personalize listing suggestions by utilizing signals such as past searches, stays, location preferences, price sensitivity, and interaction patterns. We are particularly interested in understanding whether Julia’s data science and machine learning ecosystem is mature enough for production-grade recommendation systems, how well it performs at scale, and which Julia packages (e.g., DataFrames.jl, StatsBase.jl, MLJ.jl, Flux.jl) are best suited for collaborative filtering, content-based recommendations, or hybrid approaches in a real-world marketplace scenario.

content: RadicalStart is a custom software and clone-script development company that helps startups and entrepreneurs launch proven business models faster and more affordably.

Length limit for topic headlines?
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I think the answer is… probably yes? But without more detail on the type of recommendation system you want to implement it’s hard to recommend any specific ML packages.

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I seems you just pasted some instructions you were given without bothering to change the wording.

That’s not how this forum works.

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Welcome to the Julia discourse board, @Narendran_O. Please take some time to familiarize yourself with how we talk to each other here — we’re a community of humans who enjoy talking with each other.

We are neither a coding service nor a chatbot; please don’t treat us as such.

I encourage you to ask more specific questions; this topic as you’ve posed it here isn’t a good fit for the forum.

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