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Comment by indymike | Hacker News Reader

People try to apply AI to high-risk problems that smart people can't solve. When AI is applied to lower risk probelms that are usually easy for people to solve, we seem to get great results (i.e. recommendation engines).

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Never in my life I encountered a good recommendation engine, let alone a great one.

I seem to emit some kind of anti-AI field - voice recognition works about 40% of the time so is basically useless, recommendation algorithms seem to recommend stuff I have already watched or seem completely random and as for the mechanisms that select adverts - Youtube seems to be taking the approach of showing me the same adverts again and again and again (and yes, I do click thumbs down on them) until I passionately hate the products being advertised to me (because I watch videos about cars does not mean I want to watch the same BMW 8-series advert for a few months).

The recommendation algorithms seem to be just the most simplistic type of pigeon holing. Think Netflix, just because I watched a European political thriller last week, it is assumed I now want to watch this genre for eternity.

Pandora, LibraryThing and Criticker can definitely keep up with me, a human, when it comes to recommending stuff within their specific domains.

We should keep in mind that companies and sectors set a different goal for the recommendations. Others prefer to show recommendations on similar items, others from what similar users have purchased/viewed/listened, others on highly-profitable items, others focus on discoverability of new items, etc. And as a result people view recommendations differently based on their personal taste and purpose.

And of course, we should always compare the recommendations with the usual baselines. e.g. i sometimes hate youtube recommendations, but what if the baseline was videos that are trending or are watched by users in my country? I would hate them more.

It's much easier to build a rec-engine that uses user data to make recommendations than it is to design one that analyzes intrinsic properties of items to build recommendations. Think how Spotify recommends music based on what other people who listen to this song like. This favors popular music. They could build an engine that analyzes musical characteristics to make recommendations, which would eliminate the popularity bias, but introduce others.

Actually Spotify does more than collaborative filtering. Here’s a superb blog post on using convolutional filters on the spectrograms to build content-based recommendations: https://benanne.github.io/2014/08/05/spotify-cnns.html

A more pertinent example may be an early warning system, common in finance, fraud, and ops, which flags when a statistic is an outlier of some type. These flags then get followed up on by designated folks.

Not as sexy as a general recommendation engine, but useful nonetheless.

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