Not Recommended: Why Current Content Recommendation Systems Fail Us

9 Sep

Recommendation systems paint a wonderful picture: The system automatically gets to know you and caters to your preferences. And that is indeed what happens except that the picture is warped. Warping happens for three reasons. The first is that humans want more than immediate gratification. However, the systems are designed to learn from signals that track behaviors in an environment with strong temptation and mostly learn “System 1 preferences.” The second reason is use of the wrong proxy metric. One common objective function (on content aggregation platforms like YouTube, etc.) is to maximize customer retention (a surrogate for revenue and profits). (It is likely that the objective function doesn’t vary between subscribers and ad-based tier.) And the conventional proxy for retention is time spent on a product. It doesn’t matter much how you achieve that; the easiest way is to sell Fentanyl. The third problem is the lack of good data. Conventionally, the choices of people whose judgment I trust (and the set of people whose judgments these people trust) are a great signal. But they do not make it directly into recommendations on platforms like YouTube, Netflix, etc. Worse, recommendations based on similarity in consumption don’t work as well because of the first point. And recommendations based on the likelihood of watching often reduce to recommending the most addictive content. 


  1. More Control. To resist temptation, humans plan ahead, e.g., don’t stock sugary snacks at home. By changing the environment, humans can more safely navigate the space during times when impulse control is weaker.
    • Rules. Let people write rules for the kinds of video they don’t want to be offered.
    • Source filtering. On X (formerly Twitter), for instance, you can curate your feed by choosing who to follow. (X has ‘For You’ and ‘Following’ tabs.) The user only sees tweets that the users they follow tweet or retweet. (On YouTube, you can subscribe to channels but the user sees more than the content produced by the channels they subscribe to.)
    • Time limits. Let people set time limits (for certain kinds of content).
    • Profiles Offer a way to switch between profiles.
  2. Better Data
    • Get System 2 Data. Get feedback on what people have viewed at a later time. For instance, in the history view, allow people to score their viewing history.
    • Network data. Only get content from people whose judgment you trust. This is different from #1a, which proposes allowing filtering on content producers.
  3. Information. Provide daily/weekly/monthly report cards on how much time was spent watching what kind of content, and what times of the day/week were where the person respected their self-recorded preferences (longer-term).
  4. Storefronts. Let there be a marketplace of curation services (curators). And let people visit the ‘store’ than the warehouse (and a particular version of curation).

Acknowledgment. The article benefitted from discussion with Chris Alexiuk and Brian Whetter.