Matching on Shared Interests Doesn't Work. Here's Why

Both like hiking. Both list the same obscure sitcom. Both swipe past a profile that doesn't mention coffee, because coffee felt like a dealbreaker that morning. Matching on shared interests is the oldest assumption in online dating, and it's wrong in a specific, measurable way: shared interests predict almost nothing about whether two people will actually work as a couple.

That's not a hot take. It's the conclusion of the most comprehensive academic review of online dating algorithms ever conducted, and it holds up a decade later. The problem isn't that interests don't matter at all — it's that the kind of compatibility a relationship actually needs can't be computed from a list of hobbies before two people have met.

Where the Shared-Interests Belief Comes From

The logic feels airtight: people who like the same things get along better, so an app that matches on shared interests should produce better couples than one that doesn't. Early dating sites built entire business models on this premise — extensive questionnaires, compatibility percentages, the promise of science behind the matching.

It's also intuitive in everyday life. You bond with coworkers who share your taste in music. You feel an instant rapport with a stranger who's also obsessed with the same niche sport. It's natural to assume that feeling scales into romantic compatibility — and natural for an app to turn it into a filter.

What the Research Actually Found When It Checked

Psychologist Eli Finkel and colleagues reviewed the evidence behind online dating's matching claims in a landmark paper, "Online Dating: A Critical Analysis From the Perspective of Psychological Science," published in Psychological Science in the Public Interest. Their verdict: the mathematical algorithms dating sites use, including ones built on shared interests and stated preferences, have little to no demonstrated ability to predict compatibility in actual relationships.

The core issue is what researchers call the problem of inert versus emergent traits. Shared interests are inert — they exist before two people ever interact. But relationship satisfaction is overwhelmingly driven by emergent properties: how a couple communicates under stress, how they repair after conflict, how their day-to-day rhythms mesh. None of that shows up on a list of favorite bands.

We've covered the broader findings of that review in depth in our deep dive on the Finkel algorithm study — worth reading if you want the full mechanics. This piece focuses narrowly on the interests claim specifically, because it's the one assumption almost every swipe profile is still built around.

Why Interests Feel Predictive But Aren't

Shared interests create an illusion of compatibility because they're easy to notice and easy to talk about on a first date. "You also hate cilantro" is a great icebreaker. It is not a load-bearing column in a relationship.

Social psychologists have a name for this gap: the difference between what predicts initial attraction and what predicts long-term relationship quality. A 2017 analysis by Joel, Eastwick, and Finkel, published in the Journal of Personality and Social Psychology, modeled dozens of variables — including similarity measures — against actual relationship outcomes and found that pre-relationship traits explained only a small fraction of variance in how a relationship turned out. The things that mattered most emerged only after people interacted.

In other words: shared interests might get you a good first date. They tell you almost nothing about whether the relationship will survive its first real disagreement.

What a Shared-Interest Filter Actually Optimizes For

This matters more once you notice what an interest-based filter does to the pool you see. If an app ranks or surfaces profiles partly by overlapping tags, it's optimizing for a frictionless opening message — not for a durable match. That's a reasonable thing for an app to optimize if its business depends on keeping you engaged in the deck. It's a poor proxy if what you actually want is a relationship that lasts past month three.

This is also where choice overload compounds the problem. Research on decision fatigue — most famously the "jam study" by Iyengar and Lepper, and more recently Ofcom's data on streaming and content overload — shows that more options, filtered or not, produce worse decisions, not better ones, past a surprisingly low threshold. We've written about what the jam study actually found and what Ofcom's streaming data adds to that picture. A shared-interest filter doesn't reduce the overload — it just reshuffles which irrelevant signal you're scrolling through.

What Interests Can Still Tell You, Narrowly

None of this means interests are useless. They're a reasonable signal for a few specific things, used narrowly:

• Lifestyle fit at the logistics level. Someone who travels constantly and someone who wants a quiet weekend routine will collide on scheduling, regardless of personality fit.
• Conversation starters, not compatibility proof. A shared interest can open a dialogue — it just can't close the question of whether the relationship works.
• Values adjacent to interests. Someone's relationship to their interests — how seriously they take their work, their faith, their family obligations — carries more signal than the interest itself.

The mistake isn't noticing interests. It's treating them as a compatibility score rather than a conversation starter.

What Actually Predicts a Better Outcome Instead

If inert traits like shared hobbies don't predict relationship quality, what does? The research points toward a short list, and none of it fits cleanly into a swipe profile:

1. Communication patterns under stress. How two people argue and repair matters more than what they agree on beforehand.
2. Attachment style compatibility. How someone handles closeness and distance predicts relationship satisfaction more reliably than interest overlap.
3. Life-stage and intent alignment. Wanting the same things, on the same timeline, outweighs liking the same shows.
4. Actual interaction, early. Relationship researchers consistently find that in-person or sustained interaction reveals compatibility that no pre-match questionnaire can.

Stanford sociologist Michael Rosenfeld's "How Couples Meet and Stay Together" research, which we've examined in a deep dive on what that data actually measured, found that the channel through which couples meet has shifted dramatically toward online — but it says nothing about which matching logic within those channels actually works. Meeting online got easier. Matching well didn't get solved by that shift alone.

Where This Leaves the Interest-Matching Model

Most swipe apps still lean on shared interests because it's cheap to compute and easy to display as a badge: "3 things in common." It photographs well in a pitch deck. It just doesn't do the thing it implies it does.

This is also why the swipe model's architecture is harder to fix than its matching logic. An app built to maximize engagement has little incentive to replace a crude-but-sticky signal like shared interests with something slower and more accurate — even if the slower thing works better. We've covered that incentive directly in our look at Match Group's own 10-K filings.

The recent arrival of Overtone, the no-swipe app from Hinge founder Justin McLeod with Esther Perel on its board, is a useful data point here, not because it's live yet — it isn't — but because of who's making the bet. Someone who spent over a decade optimizing a shared-interest-and-photo matching system walked away from that exact architecture. That's a signal worth taking seriously about what interests were never going to solve, regardless of which product gets there first.

How to Act On This If You're Currently Swiping

You don't need to overhaul how you date to use this. A few practical adjustments:

• Stop weighting "things in common" badges as a real filter — treat them as trivia, not evidence.
• Prioritize profiles or conversations that reveal how someone handles stress, conflict, or change over ones that just match your hobby list.
• Move to a real conversation or meeting faster — emergent compatibility only shows up in interaction, not in stated preferences.
• If you're briefing a matchmaker — human or agent-mediated — give them the texture behind your interests, not just the list. "I hike because I need unstructured time alone" tells an agent something a tag never could; we outline how to do this well in our guide to briefing an agent instead of building a profile.

Agent-mediated matchmaking, including neverswipe's approach to AI matchmaking, is built around this exact gap: learning the texture behind a preference rather than scoring the preference itself. That's a structural difference from a shared-interest tag, not just a better version of one.

Frequently Asked Questions

Do shared interests matter at all in a relationship?

They matter for day-to-day compatibility and conversation, but research consistently shows they're weak predictors of long-term relationship satisfaction compared to communication patterns and attachment style.

Why do dating apps still emphasize shared interests if the research doesn't support it?

It's cheap to compute, easy to display, and keeps engagement high. An engagement-optimized business has little incentive to replace a sticky, visible signal with a slower, more accurate one.

What should I look for instead of shared interests when evaluating a match?

How someone describes handling conflict, stress, and change tells you more than an overlapping hobby list. Life-stage and intent alignment also matter more than most profiles suggest.

Does this mean compatibility algorithms are completely useless?

No — they can filter out genuine dealbreakers and logistics mismatches. The research specifically targets the claim that interest-based scoring predicts long-term compatibility, not that filtering has zero value.

Is there a dating approach that avoids this problem?

Approaches that gather context behind a preference, rather than scoring the preference itself, are closer to what relationship research says actually matters. That's the premise behind agent-mediated matchmaking as a category.