---
title: Recommender Systems II --- So, What's Not to Love?
author: 36-462/662, Data Mining, Spring 2020
date: Lecture 23 (9 April 2020)
bibliography: locusts.bib
output: slidy_presentation
---

\[
\newcommand{\Expect}[1]{\mathbb{E}\left[ #1 \right]}
\]

## Recommender systems are everywhere these days

- Lots of big, famous businesses (e.g., Amazon, Netflix, music streaming...)
- Even more smaller businesses (e.g., lots of clothing brands)
- Some non-profits (e.g., LibraryThing, some actual libraries)
- Some things that don't immediately look like recommender systems:
  + News websites
  + "Stories to read" from Google
  + Feeds in social media (Facebook, Twitter, etc.)
  + "Up next" on YouTube
- We've seen _how_ they work

## What happens now that they're everywhere?

- Homogenization?
- Rabbit holes?
- Feedback loops?
- Discrimination?
- Getting what you wish for?
- Do they actually make a difference?

## Homogenization (I)

- If the system recommends item $i$ because it's popular, won't that just make
  $i$ more popular?
  + (Experimentally, people _do_ pay attention to most-popular rankings,
    and artificially manipulating them can indeed make things more popular:
    @Salganik-Dodds-Watts-artificial-cultural-market, @Salganik-Watts-leading-the-herd-astray)
- If everybody's getting similar recommendations, won't that make everybody's
  consumption and likes more similar?
- Potential response: This is why recommendation systems personalize!

## Homogenization (II)

- Back in the Olden Days, it was very easy to just not run across something
  Just Like the Sort of Thing You Liked
  + Minor authors, obscure bands, cult movies...
  + _Maybe_ you had a friend who shared your taste and would introduce you
  + ...Or you had a access to a whole sub-culture...
  + Or you knew where to find specialist publications _and_ could track things down
- Result: even if two people had similar _tastes_, there was often
  relatively little overlap in the details of what they consumed
  + (With obvious exceptions for when there wasn't much choice)

## Homogenization (II), cont'd

- Recommender systems change this situation
  + Nearest neighbors: people you don't know can be close to you in
    item-feature space, and then you get recommend what they indicate you'll
	probably like
  + Factor models: similar effect
  + Entirely intentional: @Shardanand-Maes-social-information-filtering was about "algorithms for automating 'word of mouth' "!

## Homogenization (II), cont'd

- Each individual will be exposed to a broader array of Stuff Like What They Like than before
- $\Rightarrow$ more diversity _within_ individuals
- Similar individuals will be exposed to _more of the same things_
- $\Rightarrow$ less diversity _across_ individuals
  + Finance analogy: if you diversify your portfolio, the assets are
    are less correlated _within_ your portfolio
  + ... but if I also diversify my portfolio, our _portfolios_ become
	_more_ correlated

## Rabbit holes, feedback loops, echo chambers

- Amplification of small initial differences:
  + Start with a small preference for (say) Pashto rap over other kinds of music
  + System connects you to lovers of Pashto rap and recommends it
  + You follow recommendations and rate more Pashto rap
  + The system recommends even more Pashto rap
  + Eventually _everything_ it recommends is just Pashto rap
- Tastes change with experience, but don't even need that effect

## Feedback loops, echo chambers

- Music, who cares?
  + You like some news stories about how political party X is awful
  + The system connects you to other X-haters
  + The system recommends more X-is-awful stories
  + You like some of them, so you're more similar to X-haters
  + Eventually _everything_ it recommends is about how X is awful


## Discrimination

- Some recommender systems use covariates in their predictions
  + Few have rules like "Don't recommend information about
    [apartments in good neighborhoods / educational opportunities /
	good jobs / ...] to [women / Jews / Muslims / blacks / Chinese / Uighurs /
	immigrants / non-Brahmins / gun owners ...]"
  + So it's unlikely that recommender systems will be _blatantly_ biased
    towards demographic groups, especially ones defined by legally-protect
	categories
  + _BUT_ lots of other variables are very good proxies for these things
    * In the USA, ZIP code is a very good predictor of race, pretty good for
	  income and education
	* ZIP+4 is an even better predictor

## Discrimination (cont'd)

- Suppose we leave out all covariates and just use item ratings
- Lots of items are really good predictors of demographic variables,
  especially in combination
  + Education, age, race, sex, sexual orientation, income...
- We'll see examples of predicting these from which website you visit [@Goel-Hofman-who-does-what-on-the-web]
- You could definitely do this from videos watched, or books read, or
  music liked (with varying error levels)
  * E.g., some movie actors are wildly more popular among certain groups than
    others
  * Same for musical genres, musicians, book genres, ...
- Recommending things liked by people who like the things you already
  like is, in part, recommending things already common in your demographic
  group

## Why might this be a problem?

- Lack of exposure to valuable information
  + Maybe not an issue with music recommendations
  + What about the news, or job ads, or how-to-apply-for-financial-aid guides?
    * _Which_ how-to-apply-for-financial-aid guides?
- Fragmentation of the public: different groups experience the same world
  very differently, because different filters are (inadvertently) applied
  + Bad because there's no common knowledge?
    * But we were just worrying about homogenization!
  + Good because isolation helps each culture grow in its own way?
    * Grow how?
  + Better than the alternative of recommendation engineers deciding for everyone what they should see?
    * But the engineers are already doing that...
- These are old, old questions of politics and ethics
  + Paternalism vs. liberty, integration vs. diversity, ...
  + It's unlikely a team of software engineers will _solve_ them...
    * ... or even a team of software engineers and data scientists...
    * ... or even a team of software engineers, data scientists and professional ethicists
  + ... but when you're dealing with political and ethical issues, it's good to recognize that, and think about what lesson there might be from history, and not pretend it's all just technical optimization
  

## What does the system actually optimize for?

- What are we really trying to predict?
  + Ratings/likes?
  + Clicks?
  + "Engagement"?
  + Purchases?
- What are we really trying to maximize?
  + Prediction accuracy?
  + Utility to users?
    * How do we measure that?
  + Revenue /profit?
    * Where does the money come from --- sales, ads, subscriptions?


## What does the system actually optimize for?

- Usually, the system's owners want to make money
- Purchase recommendations
  + If the system recommends item $k$ to person $i$, the probability of
    their want it enough to buy it is $p_{ik}$
  + The price is $r_k$ and the system owner gets a cut, $q r_k$, for purchases
- EXERCISE:

1. What's the expected revenue for recommending item $k$?
2. When will the system owner prefer to recommend item $k$ rather than item
   $l$, **even though** $p_{ik} < p_{il}$?

## What does the system actually optimize for?

Solutions:

1. Expected revenue is $q r_k p_{ik}$

2. Prefers item $k$ to item $l$ when expected revenue is higher,
\[
q r_k p_{ik} > q r_l p_{il} ~ \Rightarrow ~ \frac{r_k}{r_l} > \frac{p_{il}}{p_{ik}}
\]

- Even if $p_{il}=1 \gg p_{ik} \approx 0$, might still recommend $k$ if $r_k \gg r_l$

- Economically: the recommendation engine's economic mechanism is not "incentive compatible"
  + Maximizing revenue for the operator $\neq$ maximizing utility for the users
  + Proverbially: "If you're not paying for the service, then you're the product being solid"
- Ways out:
  + The simple capitalist solution would be to pay for recommendations
  + ...but free services out-compete
  + An alternative: professionalism
    * Like an old-style newspaper with a "wall" between the advertising department and the journalists
	* How credibly could anyone make this promise?  How could the recommenders measure utility to the users?

## Do recommendation systems do anything?

- We recommend $k$ to $i$ and $i$ says "Yes!  I love $k$!"
- Cross-validation says: triumph!
- Cynicism says: Our recommendation has _changed_ nothing
- A **recommendation** is more of an _action_ than a _prediction

## What would a successful _recommendation_ look like?

- _Without_ the recommendation, user $i$ wouldn't try item $k$
  + (Or would be very unlikely to try it, etc.)
- _With_ the recommendation, $i$ tries $k$ and likes it
- The real question isn't $\Expect{X_{ik}|\mathrm{predictors}}$
- It's $\Expect{X_{ik}|\mathrm{predictors}, do(\mathrm{recommend})} - \Expect{X_{ik}|\mathrm{predictors}, do(\mathrm{no\ recommendation})}$
- This is a causal question

## How do we answer causal questions?

- Ignore the issue
   + Bad answer but common so you're at least failing conventionally
- Observational causal inference
  + Try to match people who got the recommendation to those who didn't
  + Need to control for the variables that lead to recommendations _and_ choices
    * Don't control for anything "downstream" from the recommendation
  + Very clear example for marketing in general (rather than
    recommendation engines strictly speaking): @Rubin-Waterman-estimating-effect-of-marketing-interventions
- Experiments
  + Randomize who gets recommendations
  + Works if you can measure the outcome _without_ the recommendation
    * Better for purchases than for ratings

## Summing up

- Recommendation systems are deeply embedded into modern, online life
- They have generally-unintended effects:
  + Homogenization within groups
  + Positive feedback loops leading to amplification and echo chambers
  + Reinforcing existing group differences
- System owners (often) want to maximize something different from users
- Measuring whether they make a difference is another tricky statistical problem

## Backup: "Engagement"

![](engagement.jpg){ width=50% }

[https://twitter.com/GabrielRossman/status/1169234703414484992]

(Prof. Rossman is joking, but he's also an excellent sociologist of mass media
and social diffusion, so this isn't _entirely_ a joke)

## Backup: Increasing returns to scale

- The more users who go into each prediction, the better
  + Figure 7 from @Shardanand-Maes-social-information-filtering:

![](sm95_f7.jpg)

- $\therefore$ Systems with more users, and with more diverse users, will
  make better predictions
- $\therefore$ A sensible user will prefer to join a larger system than a
  smaller one
  + ... all else being equal...
  + ... what might not be equal?
- $\therefore$ Tendency to segment to a few large systems which are "natural
  monopolies" for segments of users

(If you want to learn to think this way, @Shapiro-Varian is old but still excellent)


## References (in addition to the background reading on the course homepage)

