Primary: Netflix
Comparison (Optional): Amazon
Netflix constantly personalizes my homepage with sections like
“Because you watched…” and “Trending Now.”
Amazon personalizes its product pages with phrases like “Customers who
bought this also bought…” and “Frequently bought together.”
Both adjust what I see based on my past activity.
If you and others watch and rate many of the same shows, Netflix
recommends what those similar users liked that you haven’t seen
yet.
It combines collaborative filtering (user–user and
item–item patterns) with content-based features
(genres, actors, themes).
Netflix also uses context like time of day, device type, and region to
fine-tune its results.
If many people who bought or viewed the same item also purchased
another, Amazon recommends that related item to you—even if you’ve never
seen it before.
This is item-to-item collaborative filtering, which is
faster and more scalable than comparing every user to every other
user.
Add a simple “Why am I seeing this?” label for
transparency.
This would give users a quick explanation of how a show or product
was recommended — for example, “Because you rated similar thrillers
highly” or “Customers who bought this also bought…” It builds trust and
helps users understand how the system works.
Give users a diversity slider (familiar ↔︎ new
content).
This would let people decide if they want more of what they already
like or to discover something new, keeping the experience flexible and
personal.
Improve cold-start onboarding by asking a few
quick preferences.
When someone first signs up, the system could ask what genres,
actors, or product types they like to get better results
faster.
Strengthen privacy controls, letting users reset
or adjust personalization data.
Some people want to start fresh or stop certain items from shaping
future recommendations — this option gives them control.
Add context awareness such as “travel mode” or
data-saving suggestions.
Recognizing when a user is on mobile data or traveling could make
recommendations lighter, more relevant, or
bandwidth-friendly.
Avoid reinforcing popularity bias and “echo chambers” by mixing
in niche, lesser-known content (the long-tail effect).
Recommending only what’s already popular can make users miss out on
smaller shows or brands. Including lesser-known options keeps things
fresh and fair for everyone.
Let users customize how much personalization they prefer for more
control over their experience.
Some people like highly personalized suggestions, while others
prefer a broader mix. Letting users adjust that level helps balance
comfort and discovery.