In content-based filtering (CBF) recommenders, when there are is no user profile, similar items are recommended an item that a user is currently inspecting. For instance, if you are looking for a comedy movie, other comedies might be recommended.

In the literature I found that often such recommenders use linear combination (i.e., weighted sums) of distances between items features to measure similarity between items.

I wonder how common the approach with weighted sum is for CBF?

Is industry using rather linear classifiers to measure similarity between items?


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