x-algo-scoring

name x-algo-scoring description Calculate and explain X algorithm engagement scores. Use when analyzing post ranking, understanding score weights, engagement potential, or why one post ranks higher than another. X Algorithm Scoring The X algorithm calculates a weighted engagement score for each post by combining predicted probabilities of 18 user actions. This score determines feed ranking. Weighted Score Formula Score = Σ(weight × P(action)) for all 18 actions + offset From home-mixer/scorers/weighted_scorer.rs : fn compute_weighted_score (candidate: &PostCandidate) -> f64 { let s : &PhoenixScores = &candidate.phoenix_scores; let vqv_weight = Self :: vqv_weight_eligibility (candidate); let combined_score = Self :: apply (s.favorite_score, p::FAVORITE_WEIGHT) + Self :: apply (s.reply_score, p::REPLY_WEIGHT) + Self :: apply (s.retweet_score, p::RETWEET_WEIGHT) + Self :: apply (s.photo_expand_score, p::PHOTO_EXPAND_WEIGHT) + Self :: apply (s.click_score, p::CLICK_WEIGHT) + Self :: apply (s.profile_click_score, p::PROFILE_CLICK_WEIGHT) + Self :: apply (s.vqv_score, vqv_weight) + Self :: apply (s.share_score, p::SHARE_WEIGHT) + Self :: apply (s.share_via_dm_score, p::SHARE_VIA_DM_WEIGHT) + Self :: apply (s.share_via_copy_link_score, p::SHARE_VIA_COPY_LINK_WEIGHT) + Self :: apply (s.dwell_score, p::DWELL_WEIGHT) + Self :: apply (s.quote_score, p::QUOTE_WEIGHT) + Self :: apply (s.quoted_click_score, p::QUOTED_CLICK_WEIGHT) + Self :: apply (s.dwell_time, p::CONT_DWELL_TIME_WEIGHT) + Self :: apply (s.follow_author_score, p::FOLLOW_AUTHOR_WEIGHT) + Self :: apply (s.not_interested_score, p::NOT_INTERESTED_WEIGHT) + Self :: apply (s.block_author_score, p::BLOCK_AUTHOR_WEIGHT) + Self :: apply (s.mute_author_score, p::MUTE_AUTHOR_WEIGHT) + Self :: apply (s.report_score, p::REPORT_WEIGHT); Self :: offset_score (combined_score) } Action Weights by Category Positive Weights (Increase Score) Action Weight Constant Signal Type Favorite FAVORITE_WEIGHT High value engagement Reply REPLY_WEIGHT High value engagement Retweet RETWEET_WEIGHT High value engagement Quote QUOTE_WEIGHT High value engagement Follow Author FOLLOW_AUTHOR_WEIGHT Very high value Share SHARE_WEIGHT Distribution signal Share via DM SHARE_VIA_DM_WEIGHT Distribution signal Share via Copy Link SHARE_VIA_COPY_LINK_WEIGHT Distribution signal Photo Expand PHOTO_EXPAND_WEIGHT Interest signal Click CLICK_WEIGHT Interest signal Profile Click PROFILE_CLICK_WEIGHT Interest signal VQV VQV_WEIGHT Video engagement (conditional) Dwell DWELL_WEIGHT Attention signal Quoted Click QUOTED_CLICK_WEIGHT Interest signal Dwell Time CONT_DWELL_TIME_WEIGHT Continuous attention Negative Weights (Decrease Score) Action Weight Constant Signal Type Not Interested NOT_INTERESTED_WEIGHT Negative signal Block Author BLOCK_AUTHOR_WEIGHT Strong negative Mute Author MUTE_AUTHOR_WEIGHT Strong negative Report REPORT_WEIGHT Strongest negative VQV Video Eligibility Video Quality View (VQV) weight only applies if video meets minimum duration: fn vqv_weight_eligibility (candidate: &PostCandidate) -> f64 { if candidate .video_duration_ms . is_some_and (|ms| ms > p::MIN_VIDEO_DURATION_MS) { p::VQV_WEIGHT } else { 0.0 // No VQV contribution for short videos or non-videos } } Score Offset Logic Handles negative combined scores to ensure proper ranking: fn offset_score (combined_score: f64 ) -> f64 { if p::WEIGHTS_SUM == 0.0 { combined_score. max ( 0.0 ) } else if combined_score < 0.0 { // Negative scores get scaled offset (combined_score + p::NEGATIVE_WEIGHTS_SUM) / p::WEIGHTS_SUM * p::NEGATIVE_SCORES_OFFSET } else { // Positive scores just add offset combined_score + p::NEGATIVE_SCORES_OFFSET } } Score Normalization After weighted scoring, scores are normalized (implementation in util/score_normalizer.rs , excluded from open source): let weighted_score = Self :: compute_weighted_score (c); let normalized_weighted_score = normalize_score (c, weighted_score); Additional Scoring Stages 1. Author Diversity Scoring Penalizes multiple posts from the same author to promote variety: // From home-mixer/scorers/author_diversity_scorer.rs fn multiplier (& self , position: usize ) -> f64 { // First post from author: full score // Second post: score × decay_factor // Third post: score × decay_factor² ( 1.0 - self .floor) * self .decay_factor. powf (position as f64 ) + self .floor } Parameters: AUTHOR_DIVERSITY_DECAY , AUTHOR_DIVERSITY_FLOOR 2. Out-of-Network Scoring Adjusts scores for posts from accounts user doesn't follow: // From home-mixer/scorers/oon_scorer.rs let updated_score = c.score. map (|base_score| match c.in_network { Some ( false ) => base_score * p::OON_WEIGHT_FACTOR, // Reduced weight _ => base_score, // Full weight for in-network }); Example Score Calculation For a post with these predicted probabilities: favorite_score : 0.12 (12% chance of like) reply_score : 0.03 (3% chance of reply) retweet_score : 0.05 (5% chance of retweet) not_interested_score : 0.02 (2% chance of negative signal) Weighted Score = 0.12 × FAVORITE_WEIGHT + 0.03 × REPLY_WEIGHT + 0.05 × RETWEET_WEIGHT + 0.02 × NOT_INTERESTED_WEIGHT (negative) + ... + offset PostCandidate Score Fields pub struct PostCandidate { pub weighted_score: Option < f64 >, // After WeightedScorer pub score: Option < f64 >, // Final score after all scorers // ... } Related Skills /x-algo-engagement - Reference for all 18 action types /x-algo-pipeline - Where scoring fits in the full pipeline