I have a dataset of 1,000 customers that bought 20 distinct phones and rated them 1-5. I have several demographic attributes for these customers (gender, age). My website offers 100 distinct devices, each with several attributes (screen size, case material, operating system, price).

For new customers, I want to recommend a ranked list of 5 devices they might enjoy. This might be based on 1) the new customer having a similar demographic profile to the 1,000 customers that gave ratings and 2) devices that were highly rated or had similar attributes to the devices that were highly rated by a similar customer.

All the examples I’ve found show how to make recommendations to similar customers for products that were rated highly by customers of similar profiles. I want to do this without limiting recommendations to the products that were rated. Instead, I want to build a system that might recommend phones I have ratings for, and also phones that haven’t been rated (ex: the phone is new, I need to get rid of inventory, or I want them to try something they might enjoy more) based on their similarity to other highly rated products by similar customers.

What is a good approach to build a recommended system for this scenario? I’ve read about content-based, collaborative filtering, and hybrid recommenders, but I can’t find a good example of this scenario. Is there a name for the type of system I am describing here?


1 Answer 1


You want to do three things:

  1. Cluster similar customers based on their profiles and their preferences for certain devices

  2. Cluster similar devices based on ratings but also device attributes

  3. Create a recommender system based on clusterings 1 and 2

Here is how I would do it:

  1. Cluster customers via usual means (PCA, K-Means, etc.) based on their demographics

  2. For each cluster develop a model that predicts the "rating" of a device based on the devices attributes (e.g. via ensemble learning, association rules, etc.). Then use the model to predict the rating of a new device for each cluster!

  3. Implement a simple recommender based on the cluster membership and a relevant device set based on ratings, this works because of step 2 each device now has a ranking. If you want to "manipulate the customer" further you can add simple rules to add devices below the relevant rating threshold if they satisfy certain criteria (price, "is on sale", etc.).



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