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I would like to create a content recommendation system based on binary click data that also takes views into account.

What content a user has been exposed to, and therefore has the chance to click on, is currently biased by a rule based system that is not always documented. I do have view data (if a user saw the content on their screen, regardless of whether it was clicked.), and am wondering how to take this into account with a traditional matrix factorization recommendation system such as this item-item approach, or if there are other other better options.

Any suggestions for implementation in Python are a bonus!

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I would not model the view data. You stated that is based on another rule-based system. If you try to model view data, you would be learning that rule-based system (not the preferences of the users). For example, if two users are both likely to view the item would just tell you about the existing system.

I suggest using just the click data. Given that a user viewed and clicked on an item, what other items are likely to be clicked on by a user.

Python has an Implicit package that implements several different popular recommendation algorithms for this type of data.

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  • $\begingroup$ Thanks - that makes sense, but my concern is that if I model only the click data I will also be learning a (more noisy) version of the rule-based system. So I would like to "control" for the views in some way. $\endgroup$ – elz Jan 7 at 16:10
  • $\begingroup$ Another way to phrase "control" is conditional probability. Conditional probability is best handled by a Bayesian framework. Applying a Bayesian framework will increase the performance of the system. But by how much? And at what cost? I would build a simple end-to-end system with performance metrics. Then decide if I need to "control" for more stuff. Every model learns a noisy representation. Generally, a bit of noise in recommender systems is helpful. $\endgroup$ – Brian Spiering Jan 7 at 19:58
  • $\begingroup$ Can you give a specific example of how you might apply a Bayesian framework to a matrix factorization approach, or other standard implicit feedback approach? (That library looks great btw, thanks) $\endgroup$ – elz Jan 9 at 16:08

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