Feed algorithms comparison: facts, examples and context

There are only about six ways to order a feed, and every product uses one of them or a blend. Comparing the six is more useful than comparing products, because a product's blend changes and the six do not. Each policy has a definite answer to who it favors, what it costs the reader, and what a publisher can do under it.

What to take away

• Six ordering policies: arrival time, relationship, predicted response, inferred interest, editorial choice, and reader-set rules. Every real feed is a weighted mix.
• No policy is neutral. Each has a winner it did not choose to have, and the winner is visible from the structure.
• The same item needs a different plan under each policy. Knowing the blend in front of you tells you which plan.

The six policies

Policy	Orders by	Who it structurally favors	What the reader gets	What the reader loses
Arrival time	When the item was posted	Whoever posts most often, and whoever posts when the reader is awake	Predictability; nothing is hidden	Anything posted while they were away, and any sense of importance
Relationship	How close the reader is to the source	Sources the reader already interacts with	Familiarity; the people they care about	New sources; anything from the edge of their graph
Predicted response	How likely the reader is to do the measured thing	Whatever produces fast, strong reactions	Things they will probably react to	Things they would value but not react to quickly
Inferred interest	Similarity to what the reader and people like them consumed	Items that resemble popular items in the reader's cluster	Discovery within their interests	Anything outside the cluster; pleasant accidents
Editorial choice	A person's or a policy's judgment	Whatever the editor values, and whoever the editor knows	Curation and accountability	Scale; personal relevance
Reader-set rules	Filters, lists, mutes and preferences the reader configured	Sources the reader bothered to organize	Control	Convenience; most readers never configure anything

Read the third column as a set of facts about structure rather than as complaints. Arrival time favors volume because volume is the only way to be present more often. Relationship favors incumbents because the graph is built from past interaction. Predicted response favors strong reactions because those are what gets predicted. None of these needed a decision; they are what each policy is.

Where the concentration comes from

Two of the six concentrate attention on their own. Predicted response and inferred interest both use past attention as evidence for future placement, so an item with a head start keeps it, a process known in network science as preferential attachment. Arrival time and relationship do not concentrate in the same way, though relationship entrenches whoever is already close.

Inferred interest is the policy most people mean by "the algorithm". Its engine, in the usual case, is some form of collaborative filtering: the reader is placed among people who behaved similarly, and shown what they consumed. Its characteristic strength is finding things no attribute would have connected; its characteristic weakness is that it cannot say anything about an item nobody has consumed. The cold start and feedback loop that follow from that are the costs of the policy, not defects of an implementation.

Comparing them on what matters to a reader

Predictability. Arrival time and reader-set rules are fully predictable. Editorial choice is predictable in character. The other three are not, and cannot be, because they depend on inference that changes as evidence accumulates.

Manipulation resistance. Arrival time is trivially gamed by volume. Predicted response is gamed by provoking reactions. Inferred interest is gamed by fake behavior, at a cost. Editorial choice is gamed by influencing the editor. Reader-set rules are hardest to game because the reader wrote them. Every policy is gameable; they differ in how expensive the gaming is.

Time sensitivity. Arrival time and relationship handle urgent material well. Predicted response and inferred interest handle it badly, because the evidence that would promote an item takes time to arrive, and an urgent item is stale by then. This is why products that need to carry news lean on the first two.

Fairness to new entrants. Arrival time is fairest: anyone can post now. Predicted response is fair in principle and harsh in practice, because the first hours decide. Inferred interest is hardest, because there is nothing to infer from. Relationship is closed until a relationship exists.

What a publisher does under each

• Arrival time. Post when your readers are present, and often. Quality matters less than presence, and that is uncomfortable but true.
• Relationship. Deepen what exists. Replies, consistency, being recognizable. Growth is slow and durable.
• Predicted response. Front-load. Make the first seconds count and make completion easy. Remember that the item is the only row you hold, and watch for the slow drift toward making whatever scored last month.
• Inferred interest. Be classifiable. Consistency of subject helps the system place you among the right readers; wandering across subjects confuses the inference.
• Editorial choice. Be known to the editor, which means being good and being findable.
• Reader-set rules. Earn your place on a list. Ask for it, once, and make it worth the reader's configuration effort.

Most feeds you meet are a blend, and the blend shifts by surface; the reasons a blend is forced at all are in the constraints every feed shares. The practical move is to estimate the blend from your own numbers: if timing dominates outcomes, arrival time is heavy; if non-followers appear early, inferred interest is; if the first hour predicts the week, predicted response is.

Common questions

Which policy is best for readers?

There is no policy that is best on every column. Reader-set rules give the most control and are used least. The honest answer is a blend with the reader able to shift its weights, which few products offer.

Why did platforms move away from arrival time?

Because it favors volume, buries anything posted at the wrong hour, and gives the platform no way to improve the measured outcome. Whether the replacement was better for readers is a separate question from whether it was better for the measured outcome.

Can I tell which policy a feed is using?

Not exactly, and not stably. You can estimate the weights from how your items behave, and that estimate is more useful than any published description because it is about your material on that surface this month.

Is editorial choice coming back?

It never fully left; policy constraints are editorial choices encoded as rules. Whether human curation at scale returns is a product question, tracked in the three layers of platform change.