[setr]

I don’t think that’s correct. 1-5 stars is sufficient. The problem is that you need reason to continuously update the values as your preferences update over time (what was once a 5-star is now a 4-star, because that last movie I saw was phenomenal)

What you need is sufficient reason to do so — the values need to actually be useful to you to make updating an act of sanity (unlike now, where it’s purely an act of futility). Feeding the algorithm is not itself sufficient (though necessary, and currently ineffective). The ideal recommendation system would encourage rating entry as a ritual act, and more importantly, rating updates an act that derives real value.

Only then will you have good data, and from good data, a dumb algorithm will suffice.

[fouc]

Relative rating could also work here, "did you enjoy this movie more than this other movie you recently saw" type of deal.

[setr]

The problem is data entry for the recommendation algorithm is insufficient incentive to constantly use it (thereby providing “truthful”, or highly-correlated, user ratings). The ratings themselves must be directly beneficial to the user, so that the user provides truthful data for their own benefit, and secondarily for the recommendation algorithm.

That is, I’d like to catalog my own list of watched movies, and their relative ratings, so that I can have a useful system (or a direct relationship to recommendations — eg More Like This), from which Netflix can scrape for their algorithms.

That is, if I’m not honest to myself, the ratings themselves will not be honest, and not properly reflect my taste.

Specifically, there must be reason to provide negative ratings in addition to positive, to capture user taste.