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In some senses, their reasoning is rational. Some models work for some situations better than others...that recommender model might work exceptionally for someo
by saosebastiao 9y ago
In some senses, their reasoning is rational. Some models work for some situations better than others...that recommender model might work exceptionally for someone trying out different brands of the same consumable product (like Natural Deodorants, for example). For other products, an accessory product recommender model might work better (like for Vacuums and vacuum attachments). And since the number of scenarios is infinite, you might as well try as many as possible and stick with all of the ones that show promise.
Which is fine until 90% of your browsing consists of scrolling past different recommended product widgets, which seems to be the case at Amazon now. At some point, taking different working models and trying to work on converging their benefits into a more monolithic model would be a huge benefit.
- boomzilla 9y agoDon't think there is much rationale behind all these models. It's more like P(would buy a vacuum | bought a vacuum) > P(would buy X | bought a vacuum) where X is a single product. Now P(would buy a vacuum | bought a vacuum) < sum(P (would buy X | bought a vacuum)) for X that is not a vacuum, but what would be the recommendation? Hey, you bought a vacuum, come back and buy some non-vacuum stuff? For most recommendation UIs, you would need a hero item that make people want to click on. It might turn out that another vacuum is probably the best item for some people to click on, and go on to buy other stuff once they are on the site.
- saosebastiao 9y agoThe reason you see such an obvious false positive in this case isn't because people who bought vacuums are likely to buy another, but rather that people who look at vacuums are likely to buy a vacuum, and the model hasn't accounted for whether you've already bought one. A different recommended might use different types of conditionals (items bought instead of items looked at, for example), and also have success in different areas (like recommending iPhone cases for iPhone owners). In order to converge the models in a Bayesian framework you'd have to deal with the combinatorial explosion of products and event conditionals which might be pretty gnarly. But some convergence work would be better than none, otherwise you end up with 20 different recommender widgets on a page. Overall I don't think amazon's approach to date has been bad...it's just time to clean up a bit.
- baddox 9y agoWhy would you need different models like "accessory recommender"? If they have enough data to conclude that purchasers of itemA are highly likely to purchase itemB, why not just recommend itemB, even if itemA and itemB are both vacuums and a few people will complain about that on technology forums?