<p>There's the method for building a recommendation system - <a href="https://arxiv.org/abs/1205.2618" rel="nofollow noreferrer">Bayesian personalized ranking from implicit feedback</a>. I also wrote <a href="https://medium.com/heyjobs-tech/building-recommendation-system-based-bayesian-personalized-ranking-using-tensorflow-2-1-b814d2704130" rel="nofollow noreferrer">an article</a> on how it can be implemented using TensorFlow.</p>
<p>There's no &quot;right&quot; answer for the question of how to transfer implicit feedback explicitly. The answer will depend on business requirements. If the task is to increase the click rate, you should try to use the clicks. If the task of increasing conversion, you need to work with purchases.</p>
Converting "Implicit" user interactions to "Explicit" user ratings for recommender systems
<p>I'm currently in the process of building a recommendation system with implicit data (e.g. clicks, views, purchases), however much of the research I've looked at seems to skip the step of &quot;aggregating implicit data&quot;. For example, how do you aggregate multiple clicks and purchases overtime into a single user rating (as is required for a standard matrix factorization model)?</p>
<p>I've been experimenting with several Matrix Factorization based methods, including Neural Collaborative Filtering, Deep Factorization Machines, LightFM, and Variational Autoencoders for Collaborative Filtering. None of these papers seem to address the issue of aggregating implicit data. They also do not discuss how to weight different types of user events (e.g. clicks vs purchase) when calculating a score.</p>
<p>For now I've been using a confidence score approach (the conference score corresponds to the count of events) as outlined in this paper: <a href="http://yifanhu.net/PUB/cf.pdf" rel="nofollow noreferrer">http://yifanhu.net/PUB/cf.pdf</a>. However this approach doesn't address incorporating other types of user events (other than clicks), nor does it address negative implicit feedback (e.g. a ton of impressions with zero clicks).</p>
<p>Anyway, I'd love some insight on this topic! Any thoughts at all would be hugely appreciated!</p>