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Sure, many of the examples ring true. However, I would refine the claim "Machine learning is fundamentally conservative" to say "Reducing a distribution of pre
by xpe 7y ago
Sure, many of the examples ring true.
However, I would refine the claim "Machine learning is fundamentally conservative" to say "Reducing a distribution of predictions to only the most likely prediction is conservative."
- jrumbut 7y agoAlso similarity is conservative. There are alternatives. For a product/content recommendation you could include the product that's maximally different, the second most similar, you could exclude products within the same category, you could choose randomly and weight by conversion rate. We need more creativity and experimentation here I believe.
- Pamar 7y ago* you could include the product that's maximally different, the second most similar, you could exclude products within the same category, you could choose randomly and weight by conversion rate* This is interesting. Has anyone tried that or knows of a website that uses this?
- xpe 7y agoThe exploration versus exploitation tradeoff is well studied in bandit algorithms and reinforcement learning in general.
- jrumbut 7y agoI have personally implemented discarding the top-n (small n, like 3-5) most similar results in a recommendation system as well as adding a totally random item in the results but it was a small scale system. The top-n removal was a small but noticeable amount better. That was in part due to the specific content in the database as well as the audience, but it's a thing to try I believe.
- goatlover 7y agoHow would Amazon determine what's maximally different from a tv or pair of shoes? Is there a consensus way that humans determine maximum difference?
- xpe 7y agoThis is a design question. One way: frame it mathematically; map products into a space and use distance as a proxy for difference. Another way: think of common human categorizations. Opposites are usually defined in terms of these categories.
- deleted 7y ago[deleted]
- goto11 7y agoI think it is more fundamental that models are trained using existing data. An autosuggestion will suggest things people have written before. Providing a larger number of predictions doesn't change that this is inherently conservative.
- xpe 7y agoRe: "I think it is more fundamental that models are trained using existing data." Yes, I agree, but this does not have the be the final word. There is a lot of room for design. For example, an autosuggestion tool can trade off between exploration (randomized recommendations) and exploitation (pure collaborative filtering).