I'm developing an IOS app that requires me to display suggested hair products after a user has searched for one. What is the best way to implement a recommendation engine and how do I figure out the algorithm?

I currently work in iOS engineering, but I have previous experience with machine learning, including recommender systems. The first thing you need to do is ensure you are collecting a lot of data. Every time a user views a product, log it to a server. Every time a user purchases a product, log it to a server. Then, you will want to aggregate this data offline and use it to build your recommender system. One common technique for doing this is called Collaborative filtering . I highly recommend you start with that. There are basically two approaches, and it doesn't hurt to try both. There is item-item similarity, which allows you to determine: users who express interest in product x may also be interested in products y and z. There is also user-user similarity, which allows you to determine: users similar to this one are interested in these products. Personally, I found the Mahout framework and tutorials very useful in learning collaborative filtering with real data: Scalable machine learning and data mining .Once you have built your recommender system offline, you can create a proof-of-concept by implementing the algorithm in Swift or Objective-C and bundling the supporting data as CSVs in your app. However, if this proves successful (and it has with companies like Amazon), then you will want to reimplement this on the server side in such a way that the recommender system continuously updates itself as new data comes in.

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now that I've shown you how to create an out-of-the-box app let's see how an admin would go and create their own custom app from scratch so assuming you have some data sets already set in the system the admin would click create dashboard then they have some dashboard templates to choose from to get started even faster but for this example let's just go into blank dashboard so I want to look at my pipeline so let's look at my opportunity data set and we want to look at the sum of amount grouped by stage and filtered on open opportunities but I want to see my opera my stage is listed in the order that they're listed in my sales process so to do that I can actually order by another measure by adding it in a compare table so let's do my maximum of sort order per stage let's filter it for my open opportunities and then let's order it ascending now that's great but now I have I have this additional measure in here that I

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I'm developing an IOS app that requires me to display suggested hair products after a user has searched for one. What is the best way to implement a recommendation engine and how do I figure out the algorithm?
I currently work in iOS engineering, but I have previous experience with machine learning, including recommender systems. The first thing you need to do is ensure you are collecting a lot of data. Every time a user views a product, log it to a server. Every time a user purchases a product, log it to a server. Then, you will want to aggregate this data offline and use it to build your recommender system. One common technique for doing this is called Collaborative filtering . I highly recommend you start with that. There are basically two approaches, and it doesn't hurt to try both. There is item-item similarity, which allows you to determine: users who express interest in product x may also be interested in products y and z. There is also user-user similarity, which allows you to determine: users similar to this one are interested in these products. Personally, I found the Mahout framework and tutorials very useful in learning collaborative filtering with real data: Scalable machine learning and data mining .Once you have built your recommender system offline, you can create a proof-of-concept by implementing the algorithm in Swift or Objective-C and bundling the supporting data as CSVs in your app. However, if this proves successful (and it has with companies like Amazon), then you will want to reimplement this on the server side in such a way that the recommender system continuously updates itself as new data comes in.

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