{ "schemaVersion": 1, "source": "MerRec captured audience and category-share results", "rows": 1919, "joinKey": "id", "categories": [ { "column": "women", "name": "Women" }, { "column": "toys", "name": "Toys & Collectibles" }, { "column": "kids", "name": "Kids" }, { "column": "men", "name": "Men" }, { "column": "home", "name": "Home" }, { "column": "electronics", "name": "Electronics" }, { "column": "beauty", "name": "Beauty" }, { "column": "vintage", "name": "Vintage & collectibles" }, { "column": "books", "name": "Books" }, { "column": "sports", "name": "Sports & outdoors" }, { "column": "other", "name": "Other" }, { "column": "handmade", "name": "Handmade" }, { "column": "garden", "name": "Garden & Outdoor" }, { "column": "arts", "name": "Arts & Crafts" }, { "column": "pets", "name": "Pet Supplies" }, { "column": "office", "name": "Office" }, { "column": "tools", "name": "Tools" } ], "featureTransform": "sqrt(view count by category / total views)", "featureShareFormula": "per-user squared Float32 sqrt category share, rounded to 6 significant digits", "queries": { "prepare_tastes": { "queryId": "871d2b4a-6ecf-46bd-8c14-6e44c2a2f1b0", "resultJson": "docs/research/merrec/results/prepare_tastes.json", "sql": "SELECT e.user_id AS id, COUNT(*) AS views, COUNT(DISTINCT e.item_id) AS items,\n AVG(i.price) AS mean_viewed_price,\n CAST(SQRT(SUM(CASE WHEN i.c0_name = 'Women' THEN 1.0 ELSE 0.0 END) / COUNT(*)) AS REAL) AS women,\n CAST(SQRT(SUM(CASE WHEN i.c0_name = 'Toys & Collectibles' THEN 1.0 ELSE 0.0 END) / COUNT(*)) AS REAL) AS toys,\n CAST(SQRT(SUM(CASE WHEN i.c0_name = 'Kids' THEN 1.0 ELSE 0.0 END) / COUNT(*)) AS REAL) AS kids,\n CAST(SQRT(SUM(CASE WHEN i.c0_name = 'Men' THEN 1.0 ELSE 0.0 END) / COUNT(*)) AS REAL) AS men,\n CAST(SQRT(SUM(CASE WHEN i.c0_name = 'Home' THEN 1.0 ELSE 0.0 END) / COUNT(*)) AS REAL) AS home,\n CAST(SQRT(SUM(CASE WHEN i.c0_name = 'Electronics' THEN 1.0 ELSE 0.0 END) / COUNT(*)) AS REAL) AS electronics,\n CAST(SQRT(SUM(CASE WHEN i.c0_name = 'Beauty' THEN 1.0 ELSE 0.0 END) / COUNT(*)) AS REAL) AS beauty,\n CAST(SQRT(SUM(CASE WHEN i.c0_name = 'Vintage & collectibles' THEN 1.0 ELSE 0.0 END) / COUNT(*)) AS REAL) AS vintage,\n CAST(SQRT(SUM(CASE WHEN i.c0_name = 'Books' THEN 1.0 ELSE 0.0 END) / COUNT(*)) AS REAL) AS books,\n CAST(SQRT(SUM(CASE WHEN i.c0_name = 'Sports & outdoors' THEN 1.0 ELSE 0.0 END) / COUNT(*)) AS REAL) AS sports,\n CAST(SQRT(SUM(CASE WHEN i.c0_name = 'Other' THEN 1.0 ELSE 0.0 END) / COUNT(*)) AS REAL) AS other,\n CAST(SQRT(SUM(CASE WHEN i.c0_name = 'Handmade' THEN 1.0 ELSE 0.0 END) / COUNT(*)) AS REAL) AS handmade,\n CAST(SQRT(SUM(CASE WHEN i.c0_name = 'Garden & Outdoor' THEN 1.0 ELSE 0.0 END) / COUNT(*)) AS REAL) AS garden,\n CAST(SQRT(SUM(CASE WHEN i.c0_name = 'Arts & Crafts' THEN 1.0 ELSE 0.0 END) / COUNT(*)) AS REAL) AS arts,\n CAST(SQRT(SUM(CASE WHEN i.c0_name = 'Pet Supplies' THEN 1.0 ELSE 0.0 END) / COUNT(*)) AS REAL) AS pets,\n CAST(SQRT(SUM(CASE WHEN i.c0_name = 'Office' THEN 1.0 ELSE 0.0 END) / COUNT(*)) AS REAL) AS office,\n CAST(SQRT(SUM(CASE WHEN i.c0_name = 'Tools' THEN 1.0 ELSE 0.0 END) / COUNT(*)) AS REAL) AS tools\nFROM merrec_events e JOIN merrec_items i ON i.item_id=e.item_id\nWHERE e.event_type='item_view' AND e.event_time < TIMESTAMP '2023-10-21'\nGROUP BY e.user_id HAVING COUNT(*) >= 20 ORDER BY id" }, "cuml_umap": { "queryId": "bf5b54b8-6b76-4ebd-af66-e0c8c6100544", "resultJson": "docs/research/merrec/results/cuml_umap.json", "sql": "SELECT * FROM cuml_umap(input => (SELECT id, COALESCE(women, CAST(0 AS REAL)) AS women, COALESCE(toys, CAST(0 AS REAL)) AS toys, COALESCE(kids, CAST(0 AS REAL)) AS kids, COALESCE(men, CAST(0 AS REAL)) AS men, COALESCE(home, CAST(0 AS REAL)) AS home, COALESCE(electronics, CAST(0 AS REAL)) AS electronics, COALESCE(beauty, CAST(0 AS REAL)) AS beauty, COALESCE(vintage, CAST(0 AS REAL)) AS vintage, COALESCE(books, CAST(0 AS REAL)) AS books, COALESCE(sports, CAST(0 AS REAL)) AS sports, COALESCE(other, CAST(0 AS REAL)) AS other, COALESCE(handmade, CAST(0 AS REAL)) AS handmade, COALESCE(garden, CAST(0 AS REAL)) AS garden, COALESCE(arts, CAST(0 AS REAL)) AS arts, COALESCE(pets, CAST(0 AS REAL)) AS pets, COALESCE(office, CAST(0 AS REAL)) AS office, COALESCE(tools, CAST(0 AS REAL)) AS tools FROM mr_tastes ORDER BY id), n_components => 2, n_neighbors => 30, min_dist => 0.1, random_state => 42) ORDER BY id;\n" }, "cuml_hdbscan": { "queryId": "1daad9d5-6b74-41cc-8e27-9cad3fa8f78c", "resultJson": "docs/research/merrec/results/cuml_hdbscan.json", "sql": "SELECT * FROM cuml_hdbscan(input => (SELECT id, COALESCE(women, CAST(0 AS REAL)) AS women, COALESCE(toys, CAST(0 AS REAL)) AS toys, COALESCE(kids, CAST(0 AS REAL)) AS kids, COALESCE(men, CAST(0 AS REAL)) AS men, COALESCE(home, CAST(0 AS REAL)) AS home, COALESCE(electronics, CAST(0 AS REAL)) AS electronics, COALESCE(beauty, CAST(0 AS REAL)) AS beauty, COALESCE(vintage, CAST(0 AS REAL)) AS vintage, COALESCE(books, CAST(0 AS REAL)) AS books, COALESCE(sports, CAST(0 AS REAL)) AS sports, COALESCE(other, CAST(0 AS REAL)) AS other, COALESCE(handmade, CAST(0 AS REAL)) AS handmade, COALESCE(garden, CAST(0 AS REAL)) AS garden, COALESCE(arts, CAST(0 AS REAL)) AS arts, COALESCE(pets, CAST(0 AS REAL)) AS pets, COALESCE(office, CAST(0 AS REAL)) AS office, COALESCE(tools, CAST(0 AS REAL)) AS tools FROM mr_tastes ORDER BY id), min_cluster_size => 30, min_samples => 10) ORDER BY id;\n" } }, "trustworthiness": { "value": 0.964643, "nNeighbors": 15, "inputDimensions": 17, "interpretation": "descriptive UMAP neighborhood preservation against the original 17-dimensional inputs" }, "noiseRows": 1316, "assignedRows": 603, "clusterCount": 5, "clusters": [ { "clusterId": -1, "label": "Noise", "size": 1316, "theme": null, "dominantCategory": "women", "meanSquaredFeatureShare": 0.289541, "meanSquaredFeatureShares": [ 0.289541, 0.110822, 0.0835685, 0.12852, 0.104452, 0.0532919, 0.0633482, 0.0507836, 0.0300603, 0.0242398, 0.0227221, 0.0156871, 0.00516091, 0.00589507, 0.00499465, 0.00399642, 0.00291511 ] }, { "clusterId": 0, "label": "Cluster 0", "size": 65, "theme": "Electronics", "dominantCategory": "electronics", "meanSquaredFeatureShare": 0.887985, "meanSquaredFeatureShares": [ 0.00842068, 0.0386222, 0.00200717, 0.00747929, 0.00940585, 0.887985, 0.000200791, 0.0207946, 0.00694931, 0.000947389, 0.010903, 0.00275584, 0.000100227, 0.000384615, 6.65425e-05, 0.00197716, 0.000999774 ] }, { "clusterId": 1, "label": "Cluster 1", "size": 230, "theme": "Toys & Collectibles", "dominantCategory": "toys", "meanSquaredFeatureShare": 0.865589, "meanSquaredFeatureShares": [ 0.0230241, 0.865589, 0.00226923, 0.0119145, 0.0168975, 0.0399757, 0.0021308, 0.0150882, 0.00945463, 0.00155243, 0.00620043, 0.00416284, 0.000146278, 0.000353311, 0.000154618, 0.00105535, 3.11703e-05 ] }, { "clusterId": 2, "label": "Cluster 2", "size": 68, "theme": "Kids", "dominantCategory": "kids", "meanSquaredFeatureShare": 0.857174, "meanSquaredFeatureShares": [ 0.102983, 0.0149069, 0.857174, 0.01202, 0.00503988, 0.000401022, 0.000631844, 0.00119773, 0.000930982, 0.00121677, 0.00221993, 0.000542847, 0.0, 1.19755e-05, 0.000385717, 0.000312646, 2.47991e-05 ] }, { "clusterId": 3, "label": "Cluster 3", "size": 192, "theme": "Women", "dominantCategory": "women", "meanSquaredFeatureShare": 0.925826, "meanSquaredFeatureShares": [ 0.925826, 0.00177954, 0.0199506, 0.0296201, 0.00734496, 0.000780788, 0.00424154, 0.0073954, 0.000251977, 0.000385753, 0.0011546, 0.000774091, 7.7054e-05, 6.18617e-05, 0.000152526, 7.836e-05, 0.000125092 ] }, { "clusterId": 4, "label": "Cluster 4", "size": 48, "theme": "Men", "dominantCategory": "men", "meanSquaredFeatureShare": 0.865994, "meanSquaredFeatureShares": [ 0.101788, 0.00249695, 0.0213774, 0.865994, 0.000305874, 0.00138816, 0.00087971, 0.000985986, 0.000106172, 0.0043696, 0.000166338, 7.08617e-05, 0.0, 7.08617e-05, 0.0, 0.0, 0.0 ] } ], "sourceSha256": { "docs/research/merrec/features.json": 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