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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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Item-Item collaborative filtering can be applied to the unary data. This resource is good for learning item-item collaborative filtering on unary data.

In your case, you just have positives: clicks. From here, you can proceed in two ways:

  1. Binary Classification: For binary classification, you need to define "negatives". Usually implicit feedback or unary data does not have true negatives. So, in order to define your negatives, you can do a couple of things:

    • Negative Sampling: For each positive, you can sample a negative randomly
    • A view and no click as a negative: If the content was shown to the user and the user chose to not click on it counts as a negative. But, it has a selection bias of your rule-based system, which is already in place.
  2. Learning-to-rank

    Learning to rank based approaches such as BPR-MF perform well on unary data. This library is well documented for BPR-MF and works just with unary data.

  3. Learning from Multi-Channel Feedback

    If you want to learn from both views and clicks, this work comes to mind.

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  • $\begingroup$ Thank you - the view and no click as a negative is a great idea. I also like the learning to rank idea, but the main point of my question is on using the unary click data plus the viewed on screen data. Do you see any way to use BPR-MF (or other MF) where the views on screen data is used in addition to the unary click data? $\endgroup$
    – elz
    Jun 15 '20 at 15:05
  • $\begingroup$ I edited my answer to add additional work, which uses feedback from multiple channels. $\endgroup$ Jun 16 '20 at 11:01
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The simplest answer with the cleanest solution may be one that combines your binary click data and view data into another model feature that you can optimize against. What this looks like depends on your knowledge of your end-to-end system.

For instance, is it "good" that a user clicks on something with a high amount of views, or better if a user clicks on something with a low amount of views? You might have very different formulas depending on this question alone:

normalization_function((1/views) * mean(clicks)) vs perhaps normalization_function((view) * mean(clicks))

Be sure to check the assumptions of your matrix factorization implementation, but this new features may just be able to be swapped with your click data.

As a side note, I don't know exactly what you are implementing, but clicks and views generally represent different things -- as a rough guide (clicks = engagement, view = eyeballs), so it might not mean much to combine these.

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  • $\begingroup$ Thank you, this is a really interesting idea. Do you have any references for how this kind of approach performs or if it's been used before? Also, are you using views/clicks across the dataset or just the single user in your functions above? And to answer your question, I think in our case we just want the maximum number of clicks - engagement - per view. Doesn't matter if it's high or low views. $\endgroup$
    – elz
    Jun 15 '20 at 15:15
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    $\begingroup$ I don't have a document handy, nor am I sure I can find one from a literature search. If we think about it, we aren't deviating from the methodology. What we are doing is changing the definition/meaning of the underlying data to (hopefully) be more representative of your goal. Have you checked Kaggle kernels? kaggle.com/rounakbanik/movie-recommender-systems -- I didn't dig too deeply, but it looks like #2 is similar to what I'm suggesting -- two definitions of success, same method. $\endgroup$
    – ngopal
    Jun 15 '20 at 23:07
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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 '20 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. $\endgroup$ Jan 7 '20 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 '20 at 16:08

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