Dispelling Three Common Myths of Machine Learning Personalization
By: Travis Greene
Tags: AI, Advertising, Marketing, Data Science, Machine Learning

Photo by Glenn Carstens-Peters on Unsplash

Before we get all worked up about the future of AI and the inevitable singularity, we should be clear about what exactly machine learning personalization (MLP) is. It turns out that what it is and what it does is probably not what you thought. In what follows, I’ll try to explain and dispel three of the most common myths I see when reading and discussing MLP with academics and practitioners.

Keep in mind that these myths apply to collaborative filtering and hybrid approaches to personalized recommendations, which rely on behavioral big data and make up most of what we see deployed in industry today.

Myth 1: MLP Works by Predicting Your Needs, Preferences, and Desires

This misconception is understandable as we generally view persons to have both inner desires, needs, and preferences and outer-facing behavior. If we use the word personalization, then we might assume we are referring to one’s inner world of needs and preferences — that unique, narrative soup of personal history, values, goals, desires, and wants that make you, you. But we aren’t and we can’t. Many academic articles and in patents for recommender systems by companies like Google and IBM make this mistake. For example, a highly cited paper by Basu et al. (1998) states:

This paper presents an inductive learning approach to recommendation that is able to use both ratings information and other forms of information about each artifact in predicting user preferences.

Or another, more recent example from the first sentence of Yeomans et al. (2019):

Computer algorithms are increasingly being used to predict people’s preferences and make recommendations.

I can very quickly tell you why this cannot possibly be how MLP works. Preferences don’t buy things. Needs don’t click ads. Desires don’t churn. People do these things and these things are recorded in the form of observable behaviors. The training data used in MLP is really just a thin slice of your observed behavior, behavior which is afforded by the design of the app or device and happens to be measured. Smart designers and data scientists can collect the right kind of measurements to make the inferential leap from observed behavior to mental state fairly accurate, however.

Another reason why MLP can’t predict your preferences is because we have no method for actually knowing what your “true” preferences are. Do we mean your considered, conscious, verbally-reported preferences, or those unconscious goal-directed preferences we share with our evolutionary ancestors? Without a ground truth, we cannot compute a loss function and we therefore cannot optimize the parameters of the predictive model to minimize this cost function.

Despite this discrepancy, many in industry and academia have seemingly fallen into the trap of radical behaviorism, whether they’re aware of it or not.

Here’s a more concrete example. When you train a machine learning model, there is no outcome column labeled “needs” or “interests” and a list of possible discrete values such a variable might have, such as “toilet paper” or “trip to Italy.” Instead the outcome column will simply say “Buy” or “Add” or “Churn” and the value for these columns will be typically either a 1/0. These are all very narrowly defined behaviors that are the result of a near infinity of prior mental states. But, strictly speaking, mental states are not equivalent to behaviors. (See Daniel Dennett’s Inverted Spectrum thought experiment for a nice example of why the “meaning” of behaviors is over-determined.)

Conflating a behavior with a mental state is sloppy thinking and, at worst, scares laypeople into believing that machines can predict their thoughts. MLP should not be mistaken for a predictive theory of mind.

Myth 2: Personalized Recommendations are Unique to You

This myth might take the most unpacking, and there are several angles to this, but I will focus here on just a couple. In many cases, the predictions and recommendations are based on models trained (optimized) on aggregate data that may not even include any of your personal data. If you can be said to receive a “personalized” recommendation or prediction at all, then it is only because recommendations were not made using a pre-set list and given to all at once. This is roughly how advertising was done prior to the Internet, when everyone saw the same billboards and newspaper ads. Let’s call this the “naive view” of MLP.

Many people seem to believe that if each row in a data table is assigned a prediction (instead of globally assigning one to everyone, say based on a global average), then that prediction is personalized. But this is an extremely thin understanding of personalization. The concept of personalization deserves a much richer examination.

A New Taxonomy For Evaluating Personalization

I suggest we instead think of personalization using a dual taxonomy of properties of either the 1) data or 2) the model, or some combination of both. Data used in MLP can be broken down into input and output properties. For example, a personalized recommendation uses X% behavioral input features, or is the proportion of input features classified as personal data under the GDPR. It stands to reason that personalization requires personal data, and personal data are defined differently according to the particular legal regime (GDPR or CCPA or FTC’s Fair Information Practice Principles, for instance).

Conversely, we might quantify the degree of personalization by reference to output data properties, such as the uniqueness of values. A more personalized prediction means that fewer people share the same recommendation. Surely a system that resulted in everyone getting the same recommendation isn’t really personalized (or is it?). This will of course depend on the size of ranked lists, the inventory size, and the number of users you’re recommending for.

Substantive vs. Procedural Personalization

Another interesting way of viewing personalization is by considering whether it is substantive or procedural. I’ve borrowed this idea from political philosophy, where scholars debate whether procedural or substantive justice if preferable. I’ll sidestep those thorny questions for now.

Substantive personalization refers to properties of the output (e.g., all unique outputs), irrespective of the process which led to this output. Procedural personalization refers to the process (e.g., all rows are input to the same process), irrespective of the particular output such a process might generate. This distinction is useful because there might be cases where we have a highly homogenous group of data subjects and, even though we have trained a model on each unique data subject’s data, we end up with the same (or very similar) output recommendations. From an outsider’s perspective, it might seem like we haven’t personalized our recommendations since nearly every data subject got the same recommendation.

But we could reply by saying our recommendations were procedurally personalized.

Various Other Ways We Might Conceptualize Personalization

The following list is neither exhaustive nor mutually exclusive. We might decide that our personalized recommendations are personalized because they combine personal data (defined under the GDPR) and each user receives a unique recommendation, for example.

With this in mind, we might also classify a prediction or recommendation as personalized based on properties of the model used to generate it. At the most basic level, learning an individual model for each user using just that user’s own data would seem to be very personalized, though not practical for most organizations. Most data controllers simply don’t collect this much behavioral data (…yet). One consequence of this view is that users with the exact same profiles would get the exact same model. Again, this would be an example of input-based procedural, not substantive, personalization.

Another approach we might take is to quantify personalization as the uniqueness of model parameter values. So if our models have different parameter values, then the resulting predictions are personalized, even if the results are the same. This would represent input-based substantive personalization. Currently, most industry models trained on aggregate data wouldn’t satisfy this criterion.

Or we might quantify personalization as the type of model used to generate the prediction: maybe some will get a neural network, while others get a random forest. Perhaps some users do not care so much about the “best possible prediction” and a linear regression would be preferable to a deep neural network (it would also be more explainable…). As long as data subjects were input into a unique procedure for assigning the particular model, we might call this model-based procedural personalization, even if the resulting recommendations were all similar (perhaps because we only can recommend a small set of items).

Finally, maybe a personalized prediction means that the model generating a personalized recommendation for you had a unique set of input features. This will increasingly occur as data subjects under the GDPR opt-out of specific forms of data collection (e.g., certain kinds of tracking cookies or GPS locations). The behavioral data you permit the data controller to collect may mean you need different models based on different feature sets, determined by regulatory pressures. We could classify this case as an instance of input-based substantive personalization.

Myth 3: MLP Knows You Better Than You Know Yourself (or Your Friends)

Be wary when researchers and industry claim that their MLP systems “outperform humans.” In some cases, the researchers may have artificially reduced the scope of the prediction context to make it more amenable to a machine. Doing this contextual sleight of hand can make MLP seem more powerful and accurate than it really is, especially when predictive performance is evaluated. For example, Yeomans et al. (2019) compared the predictions by friends and spouses to those from a simple collaborative filtering (CF) system for predicting a focal user’s ratings of jokes. The study found a basic CF system was able to predict more accurately than a friend or spouse.

Yet, the experiment included a set of 12 jokes pre-selected by the researchers. The much more difficult problem of selecting 12 jokes from a nearly infinite set of possible jokes in all cultures and languages was left to the humans. In essence, the researchers had already personalized a list of jokes to each subject in the study, given their linguistic background, country of origin, and current location. Once narrowed to such a small recommendation space, the algorithm’s performance appears quite impressive, but nevertheless hides the fact the hardest task had already been done by humans. A similar argument can be made for personalization on e-commerce sites: by going to a website, a person has already self-selected into a group who would be interested in products offered by the website. Consequently, when we hear impressive accuracy or recall scores, we need to keep in mind how specific and narrow the prediction context is.