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      "ref": "jacob34/clear-n-simple-final-shared-notebook",
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      "ref": "iwatatakuya/22nd-place-lgbm-model-single-infer",
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      "ref": "daisukenagao/0-0226-h-m-eda-customer-clustering-by-kmeans",
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      "ref": "bearcater/h-m-personalized-fashion-recommendations",
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      "ref": "ktakita/h-m-ensembling-weightoptimization",
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      "ref": "algerwang/lb-0-0240-h-m-ensemble-magic-multi-blend",
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      "ref": "tomaszporzycki/h-m-recommendation-with-seasonality",
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      "ref": "sanvidpunde/h-m-prediction-using-decision-tree",
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      "ref": "iwatatakuya/22nd-place-lgbm-model-single-train",
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      "ref": "haozhang607/h-m-personalized-fashion-recommendations",
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      "ref": "ericajingwei/h-m-eda",
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      "ref": "anejmila/h-m-eda-focusing-on-time-series-analysis",
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      "ref": "hemanthkasinadh/190031616-fashion-recommender-system-using-r",
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      "ref": "vincentbrunner/h-m-image-embeddings",
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      "ref": "tarique7/lb-0-0240-h-m-ensemble-magic-multi-blend",
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      "ref": "derposoft/0242-ensemble-model",
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      "ref": "willing2211/h-and-m-swint-image-embedding",
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      "ref": "igjit1/recommend-items-purchased-together-in-r",
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      "ref": "nealhams/lb-0-0238-h-m-ensembling-how-to-get-bronze",
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      "ref": "honglyu/bert4rec-deep-baseline",
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      "ref": "mohammedobeidat/content-based-filtering-with-pyspark",
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      "ref": "derposoft/hm-lgbm-model-with-phrase-embeddings",
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      "ref": "aruaru0/h-m-framework-with-item2vec",
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      "ref": "sachinkumar413/starter-v1-0",
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      "ref": "lorenzopagliaro01/h-m-simple-age-based-recommendation-pyspark",
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      "ref": "shionhonda/h-m-create-parquet-dataset",
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      "ref": "sussudharsan/turicreate-recommender-script",
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    {
      "ref": "beezus666/k-means-for-customers",
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    {
      "ref": "kimurayut/lgbmranker-starter-pack",
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    {
      "ref": "baekseungyun/evaluate-how-well-you-generate-the-candidate",
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    {
      "ref": "narendra/transformer-for-recsys-training",
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    {
      "ref": "srinivassateesh/recommend-customer-s-most-expensive-lb-0072",
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    {
      "ref": "kccortez17/h-m-0-data-prep",
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    {
      "ref": "gbalachandhiran/h-m-eda-rule-base-by-customer-age-noice-free",
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      "ref": "splav12341/lgbmranker",
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    {
      "ref": "aerdem4/h-m-rapids-article2vec",
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    {
      "ref": "narendra/item-recommendation-by-association",
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    {
      "ref": "kimurayut/gbm-ranking",
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    {
      "ref": "narendra/transaction-eda",
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      "ref": "shionhonda/h-m-create-downsampled-dataset",
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      "ref": "akashbiswas92/tensorflow-recommenders",
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      "ref": "medrau/h-m-bar-chart-racing",
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      "ref": "mayankk9/gnnv2",
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    {
      "ref": "hervind/h-m-how-to-evaluate-candidate",
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    {
      "ref": "marcogorelli/radek-s-lgbmranker-starter-pack",
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      "ref": "marcogorelli/radek-s-lgbmranker-starter-pack-warmup",
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      "ref": "chaigle/h-m-competition-eda",
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      "ref": "ambatibhargav/customers-who-bought-this-frequently-buy-this",
      "votes": 4
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    {
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    {
      "ref": "yuyanglim/lstm-with-pyspark",
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      "ref": "kimurayut/eda-h-m-fashion-recommendations",
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    {
      "ref": "chenyubang/hm-recommend-items-always-purchased-after",
      "votes": 28
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    {
      "ref": "heyprafulla/h-m-eda-notebook",
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    {
      "ref": "saadelkouari/h-m-recommendation-system-code",
      "votes": 11
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    {
      "ref": "matt1018/h-m-trending-products-weekly-updated",
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    {
      "ref": "japneetsingh5/h-m-trending-products-weekly-updated",
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    {
      "ref": "japneetsingh5/lstm-sequential-model-updated",
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      "ref": "derposoft/hm-recbole-model",
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    {
      "ref": "marineej/notebook8b1cffbe67",
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    {
      "ref": "namanoberoi/grouping-users-and-items-basic-approach",
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    {
      "ref": "danielarmel/notebookfirstrl",
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    {
      "ref": "harsh2040/optimizing-the-memory-usage",
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      "ref": "shionhonda/h-m-optimal-period-for-counting-popularity",
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      "ref": "shigengtian/memory-trick-dataset",
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      "ref": "whutglaucous/funksvd-solution",
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      "ref": "sargisiskandaryan/image-embeddings-with-pytorch",
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      "ref": "qianyetang/h-m-ensembling-with-lstm-be7226",
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      "ref": "jaloeffe92/lb-0-0236-ensemble-gives-you-bronze-medal",
      "votes": 439
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      "ref": "yaozh66/pca-dimense-reduction-on-h-m",
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    {
      "ref": "johnakwei/h-m-previous-purchases-most-popular",
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    {
      "ref": "igjit1/simple-baseline-with-local-validation-in-r",
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    {
      "ref": "hjimbean/annotation-public-notebooks-for-the-beginner",
      "votes": 10
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    {
      "ref": "akshatpathak/h-m-recommendation",
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    {
      "ref": "beezus666/k-means-and-feature-importance-for-articles",
      "votes": 53
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    {
      "ref": "titericz/article-id-pairs-in-3s-using-cudf",
      "votes": 118
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    {
      "ref": "priyashaw15/h-m-cleaning-data",
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    {
      "ref": "hirotakanogami/h-m-cv-tutorial-jp-en",
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    {
      "ref": "djimou/h-m-personalized-fashion-analysis",
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    {
      "ref": "kirby12138/customers-who-bought-this-frequently-buy-this",
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    {
      "ref": "mirenaborisova/h-m-by-customers-age-lb-0-0227",
      "votes": 88
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    {
      "ref": "mohammedobeidat/product-similarity-with-text-embeddings",
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    {
      "ref": "sussudharsan/prediction-using-history-of-purchase",
      "votes": 9
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      "ref": "adldotori/all-in-one-sample-dataset",
      "votes": 16
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    {
      "ref": "lee1ie/data-process-for-h-m",
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    {
      "ref": "honglyu/bert4recf-onehot-encoding-bert-embedding",
      "votes": 89
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    {
      "ref": "devanshchowdhury/hm-eda-2",
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    {
      "ref": "titericz/embeddings-cluster",
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    {
      "ref": "ahmednegm22/h-m-eda-rapids-and-market-basket-analysis",
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    {
      "ref": "baekseungyun/lb-0-0235-ensemble-gives-you-bronze-medal",
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    {
      "ref": "uchiiyusaku/als-h-m",
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    {
      "ref": "hirotakanogami/h-m-eda-customer-clustering-by-kmeans",
      "votes": 111
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    {
      "ref": "figfiglol/lightfm-recommendation-system-with-h-m-data",
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      "ref": "lorenzopagliaro01/h-m-ranker-pyspark-lgbmranker",
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      "ref": "yagike525/h-m-pure-pytorch-baseline",
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    {
      "ref": "muthanaalhadrab/h-m-data",
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    {
      "ref": "krituthaisang/krit-and-oranich",
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      "ref": "sxsicha/data-sci-mo",
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    {
      "ref": "rimmir/model",
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      "ref": "rimmir/eda-analysis",
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    {
      "ref": "hanejiyuto/validate-for-astrung-insight",
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    {
      "ref": "ynmr0123/eda-correlation-between-num-of-sales-and-price",
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      "ref": "beezus666/tensor-flow-recommender-quick-start",
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    {
      "ref": "ihedot/eda-of-h-m-items-customers-transaction-data",
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      "ref": "harsh2040/h-m-with-ploty-express",
      "votes": 10
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    {
      "ref": "matveyspiridonov/h-m-little-eda",
      "votes": 15
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      "ref": "sirintrak/data-sci",
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    {
      "ref": "yshiml/h-m-eda-for-beginners",
      "votes": 65
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    {
      "ref": "supphasetengphaiboon/bst-recommendation",
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    {
      "ref": "jasonbian/apriori-and-association-rules-try-2",
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      "ref": "tbierhance/product-launches-relevant-articles-customers",
      "votes": 21
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    {
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    {
      "ref": "ceshine/h-m-recently-purchased-item-baseline",
      "votes": 37
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    {
      "ref": "pinpinutt/h-m-randomforest-model",
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    {
      "ref": "oranichj/group8-krit-oranich",
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    {
      "ref": "razvangeorgecostea/h-m-models",
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    {
      "ref": "yahelnachum/h-m-eda-mine-in-depth",
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    {
      "ref": "mdbodrulalam/h-m-ensembling-with-lstm",
      "votes": 109
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      "ref": "jacob34/cdeotte-pairs-in-3-minutes",
      "votes": 65
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      "ref": "johnnync13/gcn-recsys",
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    {
      "ref": "dnyaneshwalwadkar/pyspark-eda-for-h-m-recommendations-using",
      "votes": 9
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    {
      "ref": "tbierhance/basic-best-seller-prediction-w-cross-validation",
      "votes": 6
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    {
      "ref": "nmayank10/h-m-personalized-fashion-recommendations",
      "votes": 10
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      "ref": "sargisiskandaryan/h-m-spark",
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    {
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    {
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    {
      "ref": "sussudharsan/h-m-similar-products-recommender-script",
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    {
      "ref": "abhishekraikaushik/fork-of-h-m-submission",
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    {
      "ref": "gbalachandhiran/singular-value-decomposition-for-recommendation",
      "votes": 16
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    {
      "ref": "hechtjp/h-m-new-features-for-customers",
      "votes": 4
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    {
      "ref": "junjitakeshima/h-m-postal-code-grouping-en-jp",
      "votes": 11
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    {
      "ref": "tan0ry0/just-looking-ipynb",
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    {
      "ref": "karollaszewski/h-m-eda",
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    },
    {
      "ref": "aleksandrmorozov123/h-m-personalized-fashion-recommendations",
      "votes": 177
    },
    {
      "ref": "tbierhance/scale-images-without-distortion",
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    },
    {
      "ref": "hechtjp/h-m-eda-rule-base-by-customer-age",
      "votes": 304
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    {
      "ref": "uchiiyusaku/h-m-eda",
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    {
      "ref": "berkeakkaya/nlp-based-h-m-recsys",
      "votes": 29
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    {
      "ref": "jaloeffe92/h-m-ensembling-lb-0-0234",
      "votes": 161
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    {
      "ref": "razvangeorgecostea/h-m-eda",
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    {
      "ref": "iulian277/h-m-solution",
      "votes": 20
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    {
      "ref": "guoyonfan/improved-h-m-pure-pytorch-baseline",
      "votes": 92
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      "ref": "astrung/lstm-model-with-item-infor-fix-missing-last-item",
      "votes": 163
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    {
      "ref": "ransakaravihara/rfm-features-and-simple-eda-on-transactions",
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      "ref": "yangranran/lb0-0198-h-m-pure-pytorch-baseline-just-add-bn",
      "votes": 19
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    {
      "ref": "arseniykolmagorov/factorization-machine-v0-2",
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      "ref": "astrung/sequential-model-fixed-missing-last-item",
      "votes": 35
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    {
      "ref": "andreisaceleanu/pca-similarity-with-negative-sampling",
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      "ref": "tea0925ds/dtale-eda",
      "votes": 3
    },
    {
      "ref": "astrung/recbole-using-all-items-for-prediction",
      "votes": 61
    },
    {
      "ref": "junkoda/article-id-and-release-date",
      "votes": 15
    },
    {
      "ref": "datota/h-m-personalised-f-customer-color-category-eda",
      "votes": 11
    },
    {
      "ref": "maharanasaroj/h-m-python-simple-eda",
      "votes": 6
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    {
      "ref": "michaeltkuo/h-m-kaggle-competition-initial-practice",
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      "ref": "aerdem4/h-m-pure-pytorch-baseline",
      "votes": 382
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    {
      "ref": "deepaksadulla/h-m-recommendations-combined-features",
      "votes": 0
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      "ref": "xavi2411/factorizationmachine",
      "votes": 11
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      "ref": "iulian277/h-m-eda",
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    {
      "ref": "andreisaceleanu/eda-h-m",
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    {
      "ref": "pmanresa6/simple-baseline-with-local-validation-template",
      "votes": 18
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    {
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      "ref": "negoto/h-m-framework-for-partitioned-validation",
      "votes": 310
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    {
      "ref": "yashjaint/101903309-h-mpersonalizedfashionrecommendation",
      "votes": 0
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      "ref": "mathiscdr/content-based",
      "votes": 4
    },
    {
      "ref": "feezakhankhanzada/exploratory-data-analysis-ft-season-and-age-group",
      "votes": 19
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    {
      "ref": "jashanjotsinghbindra/h-m-101903159-datascienceass2",
      "votes": 3
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    {
      "ref": "nadianizam/h-m-fashion-recommendation-with-pyspark",
      "votes": 209
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      "ref": "andradaolteanu/h-m-eda-rapids-and-similarity-recommenders",
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      "ref": "harshitvish/ds-eval1",
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    {
      "ref": "markuslill/h-m-data-preparation",
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      "ref": "gbalachandhiran/customer-article-and-transaction-level-eda",
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      "ref": "nancysood/h-m-personalized-fashion-recommendations",
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    {
      "ref": "mohammedobeidat/h-m-predicting-next-item-with-decision-tree",
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      "ref": "atulverma/h-m-ensembling-with-lstm",
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      "ref": "zhaoxf7/ctr-model-gbdt-lr",
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      "ref": "eduardocatarino/customer-eda-age-distributions-vs-other-variable",
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      "ref": "tbierhance/article-images",
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    {
      "ref": "zhaoxf7/hnm-eda-python-project",
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    {
      "ref": "eduardocatarino/articles-eda-providing-several-insights",
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      "ref": "rickykonwar/h-m-lightfm-5articlefeatures",
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      "ref": "kahtan/comp-h-m",
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      "ref": "sawsanshakir/exploratory-data-analysis-h-m-fashion",
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      "ref": "dariussingh/h-m-recommendation-system-v1",
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      "ref": "johnnync13/factorization",
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      "ref": "maniklakherwal/let-s-do-some-eda",
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      "ref": "sgrimmr/dropping-idea-80-data-and-50-columns-drop",
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      "ref": "cyrillblache/h-m-create-candidate-articles",
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      "ref": "pyy0715/let-s-predict-the-articles-with-the-will-be-sold",
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      "ref": "pixyz0130/h-m-items-of-other-customers-buy",
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    {
      "ref": "sussudharsan/h-m-eda-understanding-the-data",
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      "ref": "labbaawwabi/h-m-project-by-awwabi",
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      "ref": "titericz/h-m-ensembling-how-to",
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      "ref": "neesha12/h-m-data-exploration-and-prediction",
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      "ref": "ravimandliya/h-m-eda-first-look",
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      "ref": "abhishekraikaushik/h-and-m",
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      "ref": "leesungreong/eda-detailed-article-text-mining",
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      "ref": "vpayen16/k2dsp-h-m",
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      "ref": "eunjeongkoo/eda-h-m-data",
      "votes": 25
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      "ref": "arunkumar1809/hm-recommender",
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    {
      "ref": "lunapandachan/h-m-byfone-s-speed-up",
      "votes": 86
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    {
      "ref": "yukou00takahashi/h-m-0-0226-faster-version",
      "votes": 90
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      "ref": "ebn7amdi/trending",
      "votes": 264
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    {
      "ref": "astrung/eda-extract-campaign-from-transactions",
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    {
      "ref": "astrung/lstm-sequential-modelwith-item-features-tutorial",
      "votes": 204
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    {
      "ref": "leesungreong/eda-customer-article-transaction-special-customer",
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    {
      "ref": "hervind/h-m-faster-trending-products-weekly",
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    {
      "ref": "rayanaay/h-m-resnet18-encoding-within-your-reach",
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    {
      "ref": "monikachivate/h-m-personalized-fashion-recommendations",
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      "ref": "infinator/h-m-fashion-visualization-with-plotly",
      "votes": 31
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    {
      "ref": "amanabdullayev/hot-products-of-interested-and-prior-week",
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    {
      "ref": "ratthachat/h-m-customer-compact-summary-micro-eda",
      "votes": 22
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    {
      "ref": "amanabdullayev/warning-sales-spike-at-end-of-september",
      "votes": 20
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      "ref": "simstar12/tabtranformer",
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    {
      "ref": "yujihirano/h-and-m-kaggle-article-seasonality",
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    {
      "ref": "nuriacami/hym-recommenders-notebook",
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      "ref": "arturxarles/task1-enricazuara-arturxarles",
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      "ref": "ikerhonorato/h-m-collaborative-np",
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      "ref": "luisrodri97/item-based-collaborative-filtering",
      "votes": 26
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    {
      "ref": "noahjadallah/final-notebook",
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      "ref": "tisonludovic/notebook69507d681f",
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      "ref": "byfone/h-m-purchases-in-a-row",
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      "ref": "salmaneunus/movie-eda-recommendation-system",
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      "ref": "kentawatanabetotosya/h-m-eda",
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    {
      "ref": "rickykonwar/h-m-lightfm-nofeatures",
      "votes": 47
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    {
      "ref": "hongeunlee/h-m-fashion-eda",
      "votes": 9
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    {
      "ref": "datark1/detailed-eda-understanding-h-m-data",
      "votes": 188
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    {
      "ref": "aussie84/clustering-based-on-image-similarity-vs-categories",
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    {
      "ref": "lichtlab/0-0226-byfone-chris-combination-approach",
      "votes": 234
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    {
      "ref": "mohammedobeidat/content-based-filtering-with-pca",
      "votes": 99
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    {
      "ref": "quinnwadas/match-em",
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    {
      "ref": "jessezhouu/h-m-predictions",
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    {
      "ref": "gbalachandhiran/eda-classes-of-rich",
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    {
      "ref": "tbierhance/basic-data-prep-memory-performance-saver",
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    {
      "ref": "salmaneunus/h-m-personalized-recommendation-simple-eda",
      "votes": 20
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    {
      "ref": "yunlinlew/h-m-online-store-sales-customer-analysis",
      "votes": 4
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    {
      "ref": "rickykonwar/h-m-exploratorydataanalysis",
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      "ref": "tarique7/hnm-exponential-decay-with-alternate-items",
      "votes": 124
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      "ref": "nadianizam/h-m-fashion-eda",
      "votes": 160
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    {
      "ref": "khushbuagrawal/h-m-data-analysis",
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    {
      "ref": "varshapalatse/h-m-eda-getting-started",
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    {
      "ref": "vladislavnikolov/item-based-recommender",
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    {
      "ref": "astrung/recbole-lstm-sequential-for-recomendation-tutorial",
      "votes": 212
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    {
      "ref": "alexvishnevskiy/gbm-ranking",
      "votes": 259
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      "ref": "jasonbian/apriori-and-association-rules",
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    {
      "ref": "mirenaborisova/h-m-eda-01",
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    {
      "ref": "yahelnachum/h-m-eda-mine",
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      "ref": "junjitakeshima/h-m-easy-grouping-by-sex-attribute-age-en-jp",
      "votes": 114
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      "ref": "hervind/h-m-generate-item-pairs",
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    {
      "ref": "mayukh18/svd-model-reranking-implicit-to-explicit-feedback",
      "votes": 165
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      "ref": "ransakaravihara/item-similarity-for-content-based-recommendation",
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      "ref": "alexvishnevskiy/ranking-item-features",
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      "ref": "aussie84/create-customer-attribute-dataset",
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      "ref": "robyngiraldez/h-m-exploratory-analysis",
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      "ref": "hechtjp/eda-based-on-timeseries",
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    {
      "ref": "susnato/h-m-lightfm",
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      "ref": "alexvishnevskiy/ranking-user-features",
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      "ref": "johnakwei/h-m-recommendation-coding-in-r",
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      "ref": "rudiboiii/paired-items-by-sex-0-0212",
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      "ref": "johnnync13/collaborative-recsys-based-item",
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      "ref": "shigeeeru/add-gender-feature",
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    {
      "ref": "hongeunlee/h-m-recommendation-with-similarity-of-articles",
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      "ref": "kavyagowdala/h-m-data-analysis",
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      "ref": "vivihu19/notebookde0157f7f7",
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    {
      "ref": "melodyyiphoiching/h-m-data-exploration-visualization",
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    {
      "ref": "mdjafrilalamshihab/h-m-magic-of-eda-awesome-score",
      "votes": 16
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      "ref": "maricinnamon/h-m-fashion-eda-predictions",
      "votes": 67
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      "ref": "thefreezer/new-h-m",
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    {
      "ref": "stassl/h-m-interactive-product-embeddings-explorer",
      "votes": 26
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    {
      "ref": "sachin/popular-products",
      "votes": 2
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    {
      "ref": "pranalibose/h-m-eda-getting-started",
      "votes": 8
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    {
      "ref": "melodyyiphoiching/h-m-recommendations",
      "votes": 9
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    {
      "ref": "priyankabhargava/h-m-dataset",
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    {
      "ref": "lunapandachan/h-m-eda",
      "votes": 96
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    {
      "ref": "konradb/product-embeddings",
      "votes": 67
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    {
      "ref": "kazukikomori/h-m-eda-v1-simple",
      "votes": 0
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      "ref": "byfone/h-m-purchase-history-optimization",
      "votes": 7
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    {
      "ref": "masaponto/h-m-rgcn-annoy",
      "votes": 11
    },
    {
      "ref": "byfone/h-m-trending-products-weekly",
      "votes": 359
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    {
      "ref": "takanobu0210/h-m-customer-only-women",
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      "ref": "iseurie/hmrec-image-encoder",
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    {
      "ref": "jasonbian/h-m-personalized-fashion-model-1",
      "votes": 0
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    {
      "ref": "cdeotte/recommend-items-purchased-together-0-021",
      "votes": 1026
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    {
      "ref": "mayukh18/time-decaying-popularity-benchmark-0-0216",
      "votes": 259
    },
    {
      "ref": "bhavinmoriya/customers-who-bought-this-frequently-buy-this",
      "votes": 0
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    {
      "ref": "jinyingjie/notebook4731f5ba49",
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    {
      "ref": "shionhonda/h-m-which-algorithm-is-the-best-ask-recbole",
      "votes": 55
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    {
      "ref": "aussie84/product-profiling-and-price-trend-analysis",
      "votes": 27
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    {
      "ref": "sameersanjaybhosale/h-m-customer-data-eda",
      "votes": 1
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    {
      "ref": "masaponto/similar-department-name-but-different-number",
      "votes": 1
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    {
      "ref": "davidedwards1/h-m-seasonality-v3",
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    {
      "ref": "souamesannis/tips-to-work-efficiently-with-the-dataset",
      "votes": 55
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    {
      "ref": "nandeshwar/mean-average-precision-map-k-metric-explained-code",
      "votes": 61
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    {
      "ref": "pankajkumar/know-your-customer",
      "votes": 1
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    {
      "ref": "anshumoudgil/h-m-ecommerce",
      "votes": 13
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    {
      "ref": "joatom/hm-relationship-of-article-columns",
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    {
      "ref": "hiroshisakiyama/recommending-items-recently-bought",
      "votes": 29
    },
    {
      "ref": "revathiprakash/h-m-eda-product-customer-trends",
      "votes": 14
    },
    {
      "ref": "cdeotte/customers-who-bought-this-frequently-buy-this",
      "votes": 447
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    {
      "ref": "bell2psy/h-m-prediction-eda-version0-1",
      "votes": 7
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    {
      "ref": "pascalparlindungang/h-m-eda-transaction-purchases-in-2019",
      "votes": 7
    },
    {
      "ref": "masaponto/h-m-lightgbm-train-and-feature-importance",
      "votes": 49
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    {
      "ref": "nikhilgulwade/h-m-recommendations-implicit-als-with-gpu-0-018",
      "votes": 22
    },
    {
      "ref": "ludovicocuoghi/h-m-sales-and-customers-deep-analysis",
      "votes": 182
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    {
      "ref": "sonmou/h-m-eda-introduction",
      "votes": 30
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    {
      "ref": "bipinkapri/deep-dive-articles-what-are-we-recommending",
      "votes": 4
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    {
      "ref": "madhuo/notebookf579195987",
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      "ref": "davidedwards1/h-m-eda",
      "votes": 4
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    {
      "ref": "pauljef/eda-sunburst-chart-cool-visualization",
      "votes": 36
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    {
      "ref": "george86/calculate-map-12-fast-faster-fastest",
      "votes": 16
    },
    {
      "ref": "lichtlab/do-customers-buy-the-same-products-again",
      "votes": 151
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    {
      "ref": "masaponto/h-and-m-swint-image-embedding",
      "votes": 10
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    {
      "ref": "oberfink/content-based-image-filtering-for-recommendation",
      "votes": 11
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    {
      "ref": "zhangchen123/eda-submit",
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    },
    {
      "ref": "kaiusdepaulacorrea/what-fashion-looks-like-eda-h-m",
      "votes": 11
    },
    {
      "ref": "hervind/h-m-calculate-map-12-from-public-notebooks",
      "votes": 56
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    {
      "ref": "shionhonda/h-m-overview-of-tabular-part",
      "votes": 7
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    {
      "ref": "stautxie/h-m-competition-eda",
      "votes": 7
    },
    {
      "ref": "joelqv/h-m-product-similarity-2-results",
      "votes": 17
    },
    {
      "ref": "joelqv/h-m-product-similarity-1-embeddings-knn",
      "votes": 20
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    {
      "ref": "julian3833/h-m-collaborative-filtering-user-user",
      "votes": 257
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    {
      "ref": "pietromaldini1/transaction-price-a-first-analysis",
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    {
      "ref": "paweljankiewicz/hm-create-dataset-samples",
      "votes": 148
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    {
      "ref": "amishra527/h-m-personalized-fashion",
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    {
      "ref": "ahsanzaman/h-m-exploratory-data-analysis-visualizations",
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    {
      "ref": "rashmikagamage/eda-v1",
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    {
      "ref": "derrickmwiti/simple-spark-recommender-system",
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    {
      "ref": "pietromaldini1/an-easy-baseline-weighted-top-popular",
      "votes": 11
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    {
      "ref": "bsridatta/h-m-an-eda",
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    {
      "ref": "luckypen/personalized-fashion-recommendations",
      "votes": 6
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    {
      "ref": "derrickmwiti/how-to-make-submissions-using-kaggle-s-api",
      "votes": 15
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    {
      "ref": "shubhamsindal0098/h-m-data-visualization",
      "votes": 35
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    {
      "ref": "beezus666/eda-basics",
      "votes": 4
    },
    {
      "ref": "ismailbaris/memory-efficient-image-and-data-format",
      "votes": 10
    },
    {
      "ref": "blackpouh/h-m-product-recommendations-eda",
      "votes": 26
    },
    {
      "ref": "juststas/hm-recsys-first-runs",
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    },
    {
      "ref": "arunamenon/fashion-recommendations-complete-eda",
      "votes": 6
    },
    {
      "ref": "kei96kag/h-m-data-understanding-by-r",
      "votes": 51
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    {
      "ref": "kaerunantoka/h-m-how-to-calculate-map-12",
      "votes": 183
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    {
      "ref": "cosmosankur/handm-eda-and-first-look",
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    {
      "ref": "deepakkaura/h-m-insightful-plots-and-prediction",
      "votes": 12
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    {
      "ref": "leejunseok97/h-m-personalize-fashion-recommendation",
      "votes": 10
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    {
      "ref": "stephenleo/h-m-eda-and-prediction-using-plotly",
      "votes": 7
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    {
      "ref": "alexvishnevskiy/lstm-generation",
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    {
      "ref": "yashvardhanprasad/h-m-eda-starter",
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    {
      "ref": "bipinkapri/base-recommender-imputation-eda",
      "votes": 8
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    {
      "ref": "pietromaldini1/fast-experiment",
      "votes": 10
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    {
      "ref": "masaponto/h-and-m-split-dataset-by-year",
      "votes": 4
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    {
      "ref": "aanisha07/h-m-product-recommendation-eda",
      "votes": 15
    },
    {
      "ref": "mehral/h-m-valentine-week-eda",
      "votes": 12
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    {
      "ref": "jacky2wong/h-m-projector-visualisation",
      "votes": 6
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    {
      "ref": "growbigger/fixed-article-vector-network",
      "votes": 1
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    {
      "ref": "yujihirano/h-and-m-kaggle-eda",
      "votes": 0
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    {
      "ref": "tajinderpalsingh61/h-m-eda-and-insights",
      "votes": 13
    },
    {
      "ref": "palash97/part-1-eda-data-cleaning-feature-engineering",
      "votes": 6
    },
    {
      "ref": "negoto/h-m-sales-period-of-fashion-items-with-k-means",
      "votes": 88
    },
    {
      "ref": "jacob34/popular-benchmark-in-29-lines-of-code",
      "votes": 30
    },
    {
      "ref": "ginkobalboa/h-m-word-counting",
      "votes": 17
    },
    {
      "ref": "onslaught/h-m-eda-additional-insights",
      "votes": 5
    },
    {
      "ref": "hiroshisakiyama/recommending-top-12-articles",
      "votes": 14
    },
    {
      "ref": "chiranjeevbit/h-m-personalized-recommendation-eda-wordcloud",
      "votes": 93
    },
    {
      "ref": "derrickmwiti/time-series-with-keras",
      "votes": 18
    },
    {
      "ref": "sayamray/h-m-personalized-recommendation-detailed-eda",
      "votes": 7
    },
    {
      "ref": "joelqv/h-m-exploration-baseline",
      "votes": 8
    },
    {
      "ref": "bipinkapri/h-m-data-exploration-plots-insights",
      "votes": 39
    },
    {
      "ref": "tensorchoko/h-m-personalized-eda-jp-en",
      "votes": 21
    },
    {
      "ref": "anerishingde/h-m-implicit-als-model-0-013",
      "votes": 16
    },
    {
      "ref": "thieuma06/h-m-table-eda",
      "votes": 1
    },
    {
      "ref": "mdbodrulalam/h-m-personalized-fashion-recommendations",
      "votes": 10
    },
    {
      "ref": "sravanneeli/h-m-eda",
      "votes": 13
    },
    {
      "ref": "gpreda/h-m-eda-and-prediction",
      "votes": 470
    },
    {
      "ref": "jillanisofttech/h-m-personalized-fashion-recommendation",
      "votes": 86
    },
    {
      "ref": "lightening3rd/eda-of-customers-and-articles",
      "votes": 5
    },
    {
      "ref": "gspmoreira/h-m-recsys-dataset-profiler-eda-with-rapids",
      "votes": 23
    },
    {
      "ref": "takaito/h-m-topic-model-tutorial",
      "votes": 17
    },
    {
      "ref": "chandanagopal2712/data-preparation-for-h-m-recommendation",
      "votes": 1
    },
    {
      "ref": "derrickmwiti/exploring-transactions-plotly",
      "votes": 8
    },
    {
      "ref": "mpwolke/kaggle-shopping-is-my-cardio",
      "votes": 38
    },
    {
      "ref": "hengzheng/time-is-our-best-friend-v2",
      "votes": 381
    },
    {
      "ref": "tianmin/what-do-we-know-about-sales-channel-id",
      "votes": 9
    },
    {
      "ref": "krsnewwave/standard-reco-eda",
      "votes": 3
    },
    {
      "ref": "meghaljambhale/what-s-trending-on-h-m",
      "votes": 12
    },
    {
      "ref": "mukeshmanral/why-h-m-cares-about-product-recommendation",
      "votes": 7
    },
    {
      "ref": "sanjaylalwani/h-msolution",
      "votes": 13
    },
    {
      "ref": "rupakroy/h-h-catalog-forecasting",
      "votes": 13
    },
    {
      "ref": "sayanroy729/eda-large-dataset-using-vaex-alternative-of-pd",
      "votes": 7
    },
    {
      "ref": "vishalgrover5/h-m-basic-eda-notebook",
      "votes": 8
    },
    {
      "ref": "manatoyaguchi/h-m-variational-autoencoder-with-pytorch",
      "votes": 14
    },
    {
      "ref": "lichtlab/h-m-data-deep-dive-chap-1-understand-article",
      "votes": 99
    },
    {
      "ref": "hanifansari93/starter-als-using-pyspark",
      "votes": 28
    },
    {
      "ref": "kaerunantoka/h-m-eda-w-pyspark",
      "votes": 88
    },
    {
      "ref": "tomooinubushi/folk-of-time-is-our-best-friend",
      "votes": 47
    },
    {
      "ref": "danofer/h-m-seasonal-baseline-0-004",
      "votes": 12
    },
    {
      "ref": "danofer/h-m-implicit-model-0-007",
      "votes": 1
    },
    {
      "ref": "danofer/time-eda-co-occurence-baseline-0-009",
      "votes": 9
    },
    {
      "ref": "hengzheng/time-is-our-best-friend",
      "votes": 47
    },
    {
      "ref": "vickeytomer/h-m-eda-exploration",
      "votes": 96
    },
    {
      "ref": "sindalshubham231/h-m-data-visualization",
      "votes": 2
    },
    {
      "ref": "danofer/implicit-cf-starter",
      "votes": 1
    },
    {
      "ref": "hamditarek/similar-image-cnn-cosine-similarity",
      "votes": 192
    },
    {
      "ref": "julian3833/h-m-implicit-als-model-0-014",
      "votes": 564
    },
    {
      "ref": "jarvisai7/224x224-is-the-shape-you-need",
      "votes": 13
    },
    {
      "ref": "danofer/h-m-rec-eda",
      "votes": 6
    },
    {
      "ref": "undersc0re/start-here-boom-boom",
      "votes": 8
    },
    {
      "ref": "derrickmwiti/using-fbprophet-to-forecast-the-total-price",
      "votes": 8
    },
    {
      "ref": "cedricsvrn/h-m-eda-baseline-cosine-similarity-als",
      "votes": 13
    },
    {
      "ref": "manatoyaguchi/h-m-easy-eda-sample-images-japanese-english",
      "votes": 15
    },
    {
      "ref": "vanguarde/h-m-eda-first-look",
      "votes": 1281
    },
    {
      "ref": "kalilurrahman/h-m-fashion-recommendation-analysis",
      "votes": 29
    },
    {
      "ref": "odins0n/h-m-image-resizer-dataset-256x256",
      "votes": 47
    },
    {
      "ref": "arcute/h-m-competition",
      "votes": 2
    },
    {
      "ref": "debarshichanda/understanding-mean-average-precision",
      "votes": 212
    },
    {
      "ref": "negoto/h-m-age-adjusted-and-time-discounted-bestsellers",
      "votes": 35
    },
    {
      "ref": "ishandutta/in-depth-eda-all-files-competition-understanding",
      "votes": 30
    },
    {
      "ref": "nehagupta8092/h-m-initial-data-analysis-and-insights",
      "votes": 5
    },
    {
      "ref": "growbigger/matrix-recommender-als",
      "votes": 14
    },
    {
      "ref": "julian3833/h-m-content-based-12-most-popular-items-0-007",
      "votes": 48
    },
    {
      "ref": "sytuannguyen/h-m-fast-eda-and-memory-reduction",
      "votes": 13
    },
    {
      "ref": "edwardcrookenden/eda-co-occurence-baseline-0-017",
      "votes": 56
    },
    {
      "ref": "desalegngeb/h-m-personalized-fashion-recommendations-eda",
      "votes": 29
    },
    {
      "ref": "remekkinas/h-m-eda-first-look-into-data",
      "votes": 120
    },
    {
      "ref": "tianmin/data-exploration-of-h-m-product-recommendation",
      "votes": 14
    },
    {
      "ref": "hamditarek/h-m-fashion-recommendations",
      "votes": 25
    },
    {
      "ref": "wti200/cold-start",
      "votes": 85
    },
    {
      "ref": "jakelj/h-m-fashion-eda",
      "votes": 74
    },
    {
      "ref": "abhilashawasthi/not-so-fancy-but-fast-benchmark",
      "votes": 127
    },
    {
      "ref": "hamzaghanmi/h-m-pfr-eda",
      "votes": 27
    },
    {
      "ref": "negoto/h-m-best-selling-items-catalog-like-eda",
      "votes": 88
    },
    {
      "ref": "inversion/most-common-purchase-benchmark",
      "votes": 55
    }
  ]
}