Media & Marketing

Author

Carlos Barge

Machine learning is a broad concept, so it could be twisted to fit many techniques. Collaborative filtering is more concrete: it refers to a specific procedure (albeit with many approaches) through which you use the behavior of other users to be able to recommend to one specific user.

Now, the “machine” part is clear, so collaborative filtering would qualify if it could also fit the “learning” part: does a CF engine learn? (i.e. does it improve over time as it gathers more data?). Usually, it does: if we are able to feed more behavioral data to the engine, the results are typically (though not always) more accurate, since most CF algorithms are able to improve their performance with additional data (in terms of accuracy, setting scalability issues aside, and discarding for simplicity problems associated with noise in data, saturation, etc). This is typically a landmark of machine learning: more data, more learning, better results.

One possible reason why CF is not more clearly positioned as machine learning is that its allocation to a machine learning category is imprecise. It may be supervised learning or unsupervised learning, depending on the algorithm or the data type. When supervised, it could be a regression or a classification, depending on the output. It could even be reinforcement learning. So it does not fit neatly into one single ML category.

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