Scoring and Ranking

RSSMonster keeps several scoring concepts separate so each one has a clear meaning. A score may describe one article, a source’s recent history, personal relevance, or the importance of a current story. No single score decides whether an article is allowed to exist or be read.

Core Signals

Article quality

Article quality asks: How good is this particular article?

It is a normalized 0.0–1.0 content score derived from the article’s stored quality, sentiment, and advertising scores. Feed reputation is not inserted into the article-level value.

articleQuality =
    0.50 × qualityScore
  + 0.25 × sentimentScore
  + 0.25 × advertisementScore

The persisted inputs use 0–100; the result is clamped to that range and divided by 100 once. A missing component uses the neutral-good value 70. Higher advertisementScore means less promotional content, so all three inputs have the same higher-is-better direction.

FeedTrust

FeedTrust asks: How consistently valuable has this source been as a source of articles?

It summarizes 30 days of article quality, supporting engagement, deterministic originality, and explicit negative feedback. It is user-specific and remains separate from factual verification, crawl health, and personal interest. See FeedTrust for the complete conceptual model.

Freshness

Freshness is a time-decay value derived from publication time:

freshness = exp(-ageInHours / 48)

A missing publication time produces zero. Intelligent ranking clamps the value to 0–1, which also prevents a future publication timestamp from producing an out-of-range contribution.

Personal interest

Interest is a bounded signed contribution from trusted personal evidence. Island paths separate preference strength, Island confidence and relationship confidence. Direct vectors determine Island relationships; explicit behavioral fallback preserves unrepresented likes/favorites/dislikes with recency and intent attenuation. The strongest positive and strongest negative contributions are combined without blindly summing correlated paths. Positive interest promotes relevant material; negative interest penalizes it. FeedTrust is separate.

The internal interest-scoring reference is authoritative for those formulas. Weak relationships and singleton support have less authority; missing evidence means exactly neutral interest. Unread or missing engagement is not automatically a negative signal.

Event evidence

Events contribute coverage, publisher diversity, and corroboration signals. These describe how strongly a current occurrence is supported across articles and sources. Event co-coverage is not FeedTrust duplication evidence.

coverage        = clamp01(log2(max(eventArticleCount, 1)) / 6)
sourceDiversity = clamp01(ln(sourceCount + 1) / 2.56)
sourceSpread    = clamp01(log2(max(sourceCount, 1)) / log2(8))
crossSource     = max(sourceDiversity, sourceSpread)
corroboration   = coverage × crossSource

Counts are normalized to nonnegative finite values; clamp01 maps nonfinite values to zero and bounds finite values to [0,1]. These primitives are shared by Recommended and Top Stories. An article without an Event receives zero for every Event-derived value.

Ranking Modes

Chronological

sort:desc orders newest first and sort:asc orders oldest first. These modes are available when users want a conventional RSS inbox without intelligent ranking. The optional Unread high-trust preference is a legacy override that can blend bounded FeedTrust into these chronological modes; when disabled, ordering is strictly by publication time and stable article ID.

Quality

Quality ranking combines the value of the individual article with the source’s recent record:

Quality = 0.70 × articleQuality + 0.30 × feedTrust

The two inputs remain independently visible and conceptually distinct.

First normalize finite interestScore to [-1, 1], or use zero when missing or nonfinite. Then positiveInterest = max(interestScore, 0), negativeInterest = max(-interestScore, 0), and a matching rule contributes 0.08 once regardless of how many rule tags match. The final result retains negative totals and is capped at 1. With these normalized inputs and weights, the range is -0.30–1. Recommended sorting and API serialization preserve the sign; the presentation label uses the same signed score on a percentage scale, not a probability.

The authoritative implementation is recommendedScore.js. Semantic confidence modifies interest; it is not another final weight.

Every Article eligible under the caller’s ownership, visibility and explicit filters receives a finite Recommended score. No Event, Island, vector or nonzero interest is required. The regression target is 100% Recommended coverage, while personalization coverage may legitimately be sparse.

Input	Absent-value behavior
Authorized identity and view eligibility	Required by caller; never synthesized by ranking.
Interest	Optional; zero when missing or nonfinite.
Event/corroboration	Optional; zero without an Event.
Island, embedding	Optional; not Recommended prerequisites.
Quality components	Unavailable/nonfinite article components default to 70.
FeedTrust	Missing/nonfinite trust defaults to 0.5; finite stored values remain meaningful.
Freshness	Article model uses zero for missing publication time; a plain object without freshness uses 0.5.
Matching rule tags	Optional; no boost without a match.

Recommended is computed at runtime. The separate interest updater processes canonical, unfiltered, unread Articles; that update scope does not restrict runtime Recommended ranking to unread or Island-matched Articles.

Because Quality already contains a bounded FeedTrust contribution, Recommended does not add FeedTrust again as an independent raw boost. Event coverage, cross-source evidence, and event size are not separate Recommended terms; corroboration is its only Event-derived contribution.

Supported sorts

The visible toolbar order is Newest, Oldest, Top Stories, Recommended, Quality. These are the supported sorts for search and Smart Folder expressions.

Recommended, Top Stories, and Quality are computed across the complete eligible candidate set before a result limit is applied. Equal scores fall back to publishedAt DESC, then id DESC. These computed modes use a stable ordered ID collection for incremental article loading rather than database cursor pages.

Filters Versus Ranking

Ranking changes order. It does not make an otherwise ineligible article eligible, and a low score does not delete or unsubscribe a source. Smart Folder and search filters determine eligibility before runtime ranking is applied.

See the Search Guide for the supported sort expressions and Smart Folders for reusable filtered views.