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      "ref": "mulahli/h-m-pure-pytorch-baseline-67b32a",
      "title": "H&M Pure Pytorch Baseline 67b32a",
      "source": "live",
      "kernel_id": "29524118"
    },
    {
      "ref": "mulahli/h-m-pure-pytorch-baseline-modified",
      "title": "H&M Pure Pytorch Baseline(modified)",
      "source": "live",
      "kernel_id": "29498298"
    },
    {
      "ref": "mulahli/h-m-pure-pytorch-baseline-sampled-softmax-803a85",
      "title": "H&M Pure Pytorch Baseline(sampled softmax) 803a85",
      "source": "live",
      "kernel_id": "29462342"
    },
    {
      "ref": "tao58lee/h-m-pure-pytorch-baseline-sampled-softmax",
      "title": "H&M Pure Pytorch Baseline(sampled softmax)",
      "source": "live",
      "kernel_id": "29431796"
    },
    {
      "ref": "bhatnagardaksh/data-augmentation-tracking-decaying-lr",
      "title": "Data Augmentation, Tracking & Decaying LR",
      "source": "live",
      "kernel_id": "29431006"
    },
    {
      "ref": "hazemahmedmurshedi/predict-h-m-customer-s-purchases-by-age",
      "title": "predict H&M customer's purchases by age ",
      "source": "live",
      "kernel_id": "29426798"
    },
    {
      "ref": "tinazhao/h-m-pure-pytorch-baseline-feature-tuning",
      "title": "H&M Pure Pytorch Baseline Feature Tuning",
      "source": "live",
      "kernel_id": "29404013"
    },
    {
      "ref": "hongeunlee/h-m-customer-clv",
      "title": "H&M customer CLV",
      "source": "live",
      "kernel_id": "29363876"
    },
    {
      "ref": "tao58lee/h-m-pytorch-baseline-incorp-item-features",
      "title": "H&M Pytorch Baseline(incorp item features)",
      "source": "live",
      "kernel_id": "29316413"
    },
    {
      "ref": "zethione/detailed-data-exploration-dissertation-h-m",
      "title": "Detailed_data_exploration_dissertation(H&M)",
      "source": "live",
      "kernel_id": "29221189"
    },
    {
      "ref": "sagargadeg/recommendation-system",
      "title": "Recommendation system",
      "source": "live",
      "kernel_id": "29035354"
    },
    {
      "ref": "mohammedobeidat/h-m-predict-product-features-from-images-with-ann",
      "title": "H&M: Predict Product Features from Images with ANN",
      "source": "live",
      "kernel_id": "28993313"
    },
    {
      "ref": "mohammedobeidat/h-m-predict-product-features-from-images",
      "title": "H&M: Predict product features from images",
      "source": "live",
      "kernel_id": "28902540"
    },
    {
      "ref": "franklinshih0617/h-m-pure-pytorch-baseline-with-price",
      "title": "H&M Pure Pytorch Baseline with price",
      "source": "live",
      "kernel_id": "28786209"
    },
    {
      "ref": "emphymachine/h-m-customer-segmentation-using-clustering",
      "title": "[H&M] Customer Segmentation using Clustering",
      "source": "live",
      "kernel_id": "28766593"
    },
    {
      "ref": "emphymachine/eda-practice-01-h-m-trending-analysis",
      "title": "EDA Practice 01. H&M Trending Analysis",
      "source": "live",
      "kernel_id": "28766150"
    },
    {
      "ref": "mohammedobeidat/h-m-testing-product-captioning",
      "title": " H&M: Testing Product Captioning",
      "source": "live",
      "kernel_id": "28747452"
    },
    {
      "ref": "mohammedobeidat/h-m-product-image-captioning",
      "title": "H&M: Product Image Captioning",
      "source": "live",
      "kernel_id": "28698850"
    },
    {
      "ref": "jaycoach/h-m-pure-pytorch-baseline",
      "title": "H&M Pure Pytorch Baseline",
      "source": "live",
      "kernel_id": "28602452"
    },
    {
      "ref": "hongeunlee/h-m-online-shopping-eda",
      "title": "H&M online shopping EDA",
      "source": "live",
      "kernel_id": "28467520"
    },
    {
      "ref": "tao58lee/h-m-retrieval-ranking",
      "title": "H&M_Retrieval&Ranking",
      "source": "live",
      "kernel_id": "28397093"
    },
    {
      "ref": "cyh2017/h-m-data-mining",
      "title": "H&M-data-mining",
      "source": "live",
      "kernel_id": "27992979"
    },
    {
      "ref": "tao58lee/h-m-item-item",
      "title": "H&M_Item-Item",
      "source": "live",
      "kernel_id": "27911418"
    },
    {
      "ref": "tao58lee/matrix-factorization",
      "title": "Matrix_factorization",
      "source": "live",
      "kernel_id": "27882047"
    },
    {
      "ref": "tao58lee/h-m-user-user-collaborative-filtering",
      "title": "H&M_User-User_collaborative_filtering",
      "source": "live",
      "kernel_id": "27825646"
    },
    {
      "ref": "franklinshih0617/random-forest-implementation",
      "title": "Random Forest Implementation",
      "source": "live",
      "kernel_id": "27776382"
    },
    {
      "ref": "asr9609/ric-h-m",
      "title": "Ric - H&M",
      "source": "live",
      "kernel_id": "27763976"
    },
    {
      "ref": "gazu468/classification-with-pytorch",
      "title": "Classification with pytorch",
      "source": "live",
      "kernel_id": "27738837"
    },
    {
      "ref": "xuzicang/customers-who-bought-this-frequently-buy-th-2fa14e",
      "title": "Customers Who Bought This Frequently Buy Th 2fa14e",
      "source": "live",
      "kernel_id": "27664114"
    },
    {
      "ref": "calsonnetshikulwe/last-presentation-1",
      "title": "Last Presentation 1",
      "source": "live",
      "kernel_id": "27658032"
    },
    {
      "ref": "growbigger/multi-label-classification",
      "title": "multi-label classification",
      "source": "live",
      "kernel_id": "27610429"
    },
    {
      "ref": "mohammedobeidat/recommender-system-demo-with-streamlit",
      "title": "Recommender System Demo with Streamlit ",
      "source": "live",
      "kernel_id": "27566820"
    },
    {
      "ref": "vikrampriyaniimpact/profiling",
      "title": "Profiling",
      "source": "live",
      "kernel_id": "27473257"
    },
    {
      "ref": "leejunseok97/deepfm-ctr-fn",
      "title": "DeepFM(CTR) FN?",
      "source": "live",
      "kernel_id": "27467050"
    },
    {
      "ref": "abdullachowdry/recommend-smart",
      "title": "Recommend_smart",
      "source": "live",
      "kernel_id": "27466565"
    },
    {
      "ref": "craving1030/r-version-22nd-place-lgbm-model-single-train",
      "title": "R version->22nd place LGBM Model (single) TRAIN",
      "source": "live",
      "kernel_id": "27450708"
    },
    {
      "ref": "tbierhance/testing-batch-sizes",
      "title": "Testing Batch Sizes",
      "source": "live",
      "kernel_id": "27429333"
    },
    {
      "ref": "tao58lee/h-m-repurchase-and-popular",
      "title": "H&M_Repurchase_And_Popular",
      "source": "live",
      "kernel_id": "27412576"
    },
    {
      "ref": "calsonnetshikulwe/fork-of-final-1",
      "title": "Fork of Final 1",
      "source": "live",
      "kernel_id": "27408271"
    },
    {
      "ref": "putdejudomthai/h-m-exploration-data-and-annotate-chart",
      "title": "H&M Exploration Data and Annotate chart",
      "source": "live",
      "kernel_id": "27396318"
    },
    {
      "ref": "putdejudomthai/h-m-analytics",
      "title": "H&M analytics",
      "source": "live",
      "kernel_id": "27381321"
    },
    {
      "ref": "putdejudomthai/h-m-explore-data-and-predict-recommendation",
      "title": "H&M Explore data and predict recommendation",
      "source": "live",
      "kernel_id": "27361483"
    },
    {
      "ref": "putdejudomthai/h-m-recommend-items-bought-together-past",
      "title": "H&M recommend items bought together past",
      "source": "live",
      "kernel_id": "27356384"
    },
    {
      "ref": "putdejudomthai/h-m-recommend-items-previously-purchased",
      "title": "H&M recommend items previously purchased",
      "source": "live",
      "kernel_id": "27355317"
    },
    {
      "ref": "mrugankakarte/h-m-personalization-using-gnn",
      "title": "H&M Personalization using GNN",
      "source": "live",
      "kernel_id": "27342860"
    },
    {
      "ref": "piotrgrys/proj-system-w-rozmyto-neuronowych",
      "title": "Proj, systemów rozmyto-neuronowych",
      "source": "live",
      "kernel_id": "27331441"
    },
    {
      "ref": "chiahsin3/notebook600c2a2faf",
      "title": "notebook600c2a2faf",
      "source": "live",
      "kernel_id": "27280367"
    },
    {
      "ref": "jueunjeong/3-4-cnn-lstm",
      "title": "3,4 CNN+LSTM",
      "source": "live",
      "kernel_id": "27256915"
    },
    {
      "ref": "mohammedobeidat/h-m-recommender-comparing-4-different-approaches",
      "title": "H&M Recommender: Comparing 4 Different Approaches",
      "source": "live",
      "kernel_id": "27208865"
    },
    {
      "ref": "franklinshih0617/my-h-m-exploratory-data-analysis-and-modelling",
      "title": "MY H&M Exploratory Data Analysis and modelling :)",
      "source": "live",
      "kernel_id": "27201286"
    },
    {
      "ref": "mohammedobeidat/article-embeddings-from-features",
      "title": "article embeddings from features",
      "source": "live",
      "kernel_id": "27180791"
    },
    {
      "ref": "mohammedobeidat/h-m-item-and-customer-embeddings-from-description",
      "title": "H&M: Item and Customer embeddings from Description",
      "source": "live",
      "kernel_id": "27166830"
    },
    {
      "ref": "mohammedobeidat/customer-embeddings-from-features",
      "title": "Customer Embeddings from features",
      "source": "live",
      "kernel_id": "27161721"
    },
    {
      "ref": "mohammedobeidat/h-m-tensorflow-recommender",
      "title": "H&M Tensorflow Recommender",
      "source": "live",
      "kernel_id": "27156707"
    },
    {
      "ref": "mayankk9/transformermod",
      "title": "TransformerMod",
      "source": "live",
      "kernel_id": "27061539"
    },
    {
      "ref": "mohammedobeidat/h-m-product-similarity-with-image-embeddings-knn",
      "title": "H&M: Product Similarity with Image Embeddings &KNN",
      "source": "live",
      "kernel_id": "27051962"
    },
    {
      "ref": "mohammedobeidat/h-m-product-embeddings-from-images-vgg16",
      "title": " H&M: Product Embeddings from Images VGG16",
      "source": "live",
      "kernel_id": "27025610"
    },
    {
      "ref": "igjit1/lb-0-0235-h-m-rule-base-solution-in-r",
      "title": "[LB 0.0235] H&M Rule Base Solution in R",
      "source": "live",
      "kernel_id": "26989012"
    },
    {
      "ref": "titericz/profiling-pandas-and-cudf",
      "title": "Profiling Pandas and Cudf",
      "source": "live",
      "kernel_id": "26976020"
    },
    {
      "ref": "ashwinm500/hm-final",
      "title": "hm-final",
      "source": "live",
      "kernel_id": "26947472"
    },
    {
      "ref": "jacob34/clear-n-simple-final-shared-notebook",
      "title": "Clear N' Simple - final shared notebook",
      "source": "live",
      "kernel_id": "26944862"
    },
    {
      "ref": "iwatatakuya/22nd-place-lgbm-model-single-infer",
      "title": "22nd place LGBM Model (single) INFER",
      "source": "live",
      "kernel_id": "26935613"
    },
    {
      "ref": "weipengzhang/h-m-silver-medal-solution-45-3006",
      "title": "H&M 🥈Silver Medal Solution 45/3006",
      "source": "live",
      "kernel_id": "26927814"
    },
    {
      "ref": "daisukenagao/0-0226-h-m-eda-customer-clustering-by-kmeans",
      "title": "[0.0226] H&M EDA & Customer Clustering by Kmeans  ",
      "source": "live",
      "kernel_id": "26905713"
    },
    {
      "ref": "aruaru0/h-and-m-ensamble-only-dadfc6",
      "title": "h-and-m-ensamble-only dadfc6",
      "source": "live",
      "kernel_id": "26897982"
    },
    {
      "ref": "blankaf/h-m-fashion-ensemble-v3-best",
      "title": "H&M-fashion-ensemble-v3-best",
      "source": "live",
      "kernel_id": "26894227"
    },
    {
      "ref": "narendra/hm-train-candidate-ranking-v2",
      "title": "HM train candidate ranking-V2",
      "source": "live",
      "kernel_id": "26889954"
    },
    {
      "ref": "bearcater/h-m-personalized-fashion-recommendations",
      "title": "H&M Personalized Fashion Recommendations",
      "source": "live",
      "kernel_id": "26882309"
    },
    {
      "ref": "ktakita/h-m-ensembling-weightoptimization",
      "title": "H&M Ensembling - WeightOptimization",
      "source": "live",
      "kernel_id": "26881876"
    },
    {
      "ref": "algerwang/lb-0-0240-h-m-ensemble-magic-multi-blend",
      "title": "[LB 0.0240] H&M Ensemble Magic - Multi Blend",
      "source": "live",
      "kernel_id": "26831277"
    },
    {
      "ref": "tomaszporzycki/h-m-recommendation-with-seasonality",
      "title": "H&M recommendation with seasonality",
      "source": "live",
      "kernel_id": "26819850"
    },
    {
      "ref": "sanvidpunde/h-m-prediction-using-decision-tree",
      "title": "H & M prediction using Decision Tree ",
      "source": "live",
      "kernel_id": "26808433"
    },
    {
      "ref": "iwatatakuya/22nd-place-lgbm-model-single-train",
      "title": "22nd place LGBM Model (single) TRAIN",
      "source": "live",
      "kernel_id": "26807363"
    },
    {
      "ref": "keyanding2/my-work",
      "title": "My Work",
      "source": "live",
      "kernel_id": "26806895"
    },
    {
      "ref": "haozhang607/h-m-personalized-fashion-recommendations",
      "title": "H&M Personalized Fashion Recommendations",
      "source": "live",
      "kernel_id": "26801384"
    },
    {
      "ref": "ericajingwei/h-m-eda",
      "title": "H&M --EDA ",
      "source": "live",
      "kernel_id": "26798402"
    },
    {
      "ref": "zinmurata/h-m-011-v4svd",
      "title": "H&M_011_V4SVD",
      "source": "live",
      "kernel_id": "26790029"
    },
    {
      "ref": "suneeth84/h-m-analytics-rfm-item-based-recommendation-v2",
      "title": "H&M Analytics-RFM, Item Based Recommendation-V2",
      "source": "live",
      "kernel_id": "26747561"
    },
    {
      "ref": "suneeth84/h-m-analytics-rfm-item-based-recommendation",
      "title": "H&M Analytics-RFM, Item Based Recommendation",
      "source": "live",
      "kernel_id": "26742964"
    },
    {
      "ref": "prashants2403/radek-s-lgbmranker-starter-pack",
      "title": "Radek's LGBMRanker starter-pack",
      "source": "live",
      "kernel_id": "26723549"
    },
    {
      "ref": "anejmila/h-m-eda-focusing-on-time-series-analysis",
      "title": " H&M EDA 🛍️ focusing on time_series Analysis 📈",
      "source": "live",
      "kernel_id": "26721492"
    },
    {
      "ref": "hemanthkasinadh/190031616-fashion-recommender-system-using-r",
      "title": "190031616 Fashion Recommender System using R",
      "source": "live",
      "kernel_id": "26714031"
    },
    {
      "ref": "mayuribhoyar/h-m-recommendation-mayuri-bhoyar",
      "title": "H&M Recommendation (Mayuri Bhoyar)",
      "source": "live",
      "kernel_id": "26701567"
    },
    {
      "ref": "viji1609/h-m-basic-retrieval-model-tf-recommender",
      "title": "H&M - Basic Retrieval Model - tf Recommender",
      "source": "live",
      "kernel_id": "26696559"
    },
    {
      "ref": "vincentbrunner/h-m-image-embeddings",
      "title": "H&M: image embeddings",
      "source": "live",
      "kernel_id": "26686327"
    },
    {
      "ref": "tarique7/lb-0-0240-h-m-ensemble-magic-multi-blend",
      "title": "[LB 0.0240] H&M Ensemble Magic - Multi Blend",
      "source": "live",
      "kernel_id": "26667232"
    },
    {
      "ref": "derposoft/0242-ensemble-model",
      "title": "0242_ensemble_model",
      "source": "live",
      "kernel_id": "26648330"
    },
    {
      "ref": "willing2211/h-and-m-swint-image-embedding",
      "title": "h_and_m_swint_image_embedding",
      "source": "live",
      "kernel_id": "26642278"
    },
    {
      "ref": "igjit1/recommend-items-purchased-together-in-r",
      "title": "Recommend Items Purchased Together in R",
      "source": "live",
      "kernel_id": "26631169"
    },
    {
      "ref": "nealhams/lb-0-0238-h-m-ensembling-how-to-get-bronze",
      "title": "[LB 0.0238] H&M Ensembling - How to Get Bronze",
      "source": "live",
      "kernel_id": "26630003"
    },
    {
      "ref": "honglyu/bert4rec-bert-embedding-as-item-embedding",
      "title": "Bert4Rec + BERT embedding as item embedding",
      "source": "live",
      "kernel_id": "26623239"
    },
    {
      "ref": "honglyu/bert4rec-deep-baseline",
      "title": "Bert4Rec: deep baseline",
      "source": "live",
      "kernel_id": "26622957"
    },
    {
      "ref": "chaudhariharsh/lb-0-0238-h-m-ensembling-how-to-get-bronze",
      "title": "[LB 0.0238] H&M Ensembling - How to Get Bronze",
      "source": "live",
      "kernel_id": "26558811"
    },
    {
      "ref": "windownapat/hm-eda",
      "title": "HM EDA",
      "source": "live",
      "kernel_id": "26549112"
    },
    {
      "ref": "mohammedobeidat/content-based-filtering-with-pyspark",
      "title": "Content Based Filtering with PySpark",
      "source": "live",
      "kernel_id": "26526328"
    },
    {
      "ref": "derposoft/hm-lgbm-model-with-phrase-embeddings",
      "title": "hm_lgbm_model_with_phrase_embeddings",
      "source": "live",
      "kernel_id": "26516604"
    },
    {
      "ref": "akshitkeoliya/lb-0-0236-ensemble-gives-you-bronze-medal",
      "title": "[LB 0.0236] Ensemble gives you Bronze medal 🥉",
      "source": "live",
      "kernel_id": "26512555"
    },
    {
      "ref": "aruaru0/h-m-framework-with-item2vec",
      "title": "H&M:Framework with item2vec",
      "source": "live",
      "kernel_id": "26504447"
    },
    {
      "ref": "ajaypalsinghlo/h-m-recommendtions",
      "title": "H&M_Recommendtions",
      "source": "live",
      "kernel_id": "26501682"
    },
    {
      "ref": "sachinkumar413/starter-v1-0",
      "title": "Starter | v1.0",
      "source": "live",
      "kernel_id": "26491677"
    },
    {
      "ref": "derposoft/hm-data-recbole-generator",
      "title": "hm_data_recbole_generator",
      "source": "live",
      "kernel_id": "26490709"
    },
    {
      "ref": "peterpetrov826/fork-of-using-recbole",
      "title": "Fork of using RecBole",
      "source": "live",
      "kernel_id": "26471464"
    },
    {
      "ref": "lorenzopagliaro01/h-m-simple-age-based-recommendation-pyspark",
      "title": "H&M simple age based recommendation [Pyspark]",
      "source": "live",
      "kernel_id": "26468581"
    },
    {
      "ref": "shionhonda/h-m-create-parquet-dataset",
      "title": "H&M: Create Parquet Dataset",
      "source": "live",
      "kernel_id": "26463339"
    },
    {
      "ref": "shionhonda/h-m-create-pickled-dataset",
      "title": "H&M: Create Pickled Dataset",
      "source": "live",
      "kernel_id": "26462940"
    },
    {
      "ref": "sussudharsan/turicreate-recommender-script",
      "title": "Turicreate recommender Script",
      "source": "live",
      "kernel_id": "26451302"
    },
    {
      "ref": "beezus666/k-means-for-customers",
      "title": "k-means for customers",
      "source": "live",
      "kernel_id": "26433931"
    },
    {
      "ref": "kimurayut/lgbmranker-starter-pack",
      "title": "【日本語】【コード分析】LGBMRanker starter-pack ",
      "source": "live",
      "kernel_id": "26415859"
    },
    {
      "ref": "baekseungyun/evaluate-how-well-you-generate-the-candidate",
      "title": "Evaluate how well you generate the candidate",
      "source": "live",
      "kernel_id": "26413420"
    },
    {
      "ref": "narendra/transformer-for-recsys-training",
      "title": "Transformer for Recsys - training",
      "source": "live",
      "kernel_id": "26407600"
    },
    {
      "ref": "srinivassateesh/recommend-customer-s-most-expensive-lb-0072",
      "title": " Recommend customer's most expensive [ LB:.0072]",
      "source": "live",
      "kernel_id": "26398059"
    },
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      "ref": "djimou/h-m-personalized-fashion-analysis",
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      "ref": "mirenaborisova/h-m-by-customers-age-lb-0-0227",
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      "ref": "nmayank10/h-m-personalized-fashion-recommendations",
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      "ref": "gbalachandhiran/singular-value-decomposition-for-recommendation",
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      "ref": "tan0ry0/just-looking-ipynb",
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      "ref": "karollaszewski/h-m-eda",
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      "source": "live",
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      "ref": "aleksandrmorozov123/h-m-personalized-fashion-recommendations",
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      "kernel_id": "25596761"
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      "ref": "tbierhance/scale-images-without-distortion",
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      "kernel_id": "25591195"
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      "ref": "hechtjp/h-m-eda-rule-base-by-customer-age",
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      "kernel_id": "25577550"
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      "ref": "uchiiyusaku/h-m-eda",
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      "ref": "berkeakkaya/nlp-based-h-m-recsys",
      "title": "NLP based H&M RecSys",
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      "title": "H&M Ensembling[LB 0.0234]",
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      "ref": "iulian277/h-m-solution",
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      "kernel_id": "25546785"
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      "ref": "guoyonfan/improved-h-m-pure-pytorch-baseline",
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      "ref": "ransakaravihara/rfm-features-and-simple-eda-on-transactions",
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      "ref": "arseniykolmagorov/factorization-machine-v0-2",
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      "ref": "astrung/sequential-model-fixed-missing-last-item",
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      "ref": "astrung/recbole-using-all-items-for-prediction",
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      "kernel_id": "25482823"
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    {
      "ref": "junkoda/article-id-and-release-date",
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      "ref": "datota/h-m-personalised-f-customer-color-category-eda",
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      "ref": "maharanasaroj/h-m-python-simple-eda",
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      "ref": "michaeltkuo/h-m-kaggle-competition-initial-practice",
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      "ref": "aerdem4/h-m-pure-pytorch-baseline",
      "title": "H&M Pure Pytorch Baseline",
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      "kernel_id": "25460201"
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      "ref": "deepaksadulla/h-m-recommendations-combined-features",
      "title": "h&m-recommendations-combined-features",
      "source": "live",
      "kernel_id": "25450546"
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      "ref": "xavi2411/factorizationmachine",
      "title": "FactorizationMachine",
      "source": "live",
      "kernel_id": "25437535"
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      "ref": "iulian277/h-m-eda",
      "title": "h&m_eda",
      "source": "live",
      "kernel_id": "25432150"
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      "ref": "andreisaceleanu/eda-h-m",
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      "source": "live",
      "kernel_id": "25430291"
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    {
      "ref": "pmanresa6/simple-baseline-with-local-validation-template",
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      "ref": "bhavyavij/101903532-dslabeval",
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      "ref": "pjain2001/notebook3cdf7b5331",
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      "ref": "negoto/h-m-framework-for-partitioned-validation",
      "title": "H&M : Framework for Partitioned Validation",
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      "ref": "yashjaint/101903309-h-mpersonalizedfashionrecommendation",
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      "kernel_id": "25382581"
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    {
      "ref": "mathiscdr/content-based",
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      "source": "live",
      "kernel_id": "25381461"
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      "ref": "feezakhankhanzada/exploratory-data-analysis-ft-season-and-age-group",
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      "kernel_id": "25380037"
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      "ref": "jashanjotsinghbindra/h-m-101903159-datascienceass2",
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      "kernel_id": "25369098"
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    {
      "ref": "nadianizam/h-m-fashion-recommendation-with-pyspark",
      "title": "👗🥼H & M Fashion(Recommendation with Pyspark)👗🥼",
      "source": "live",
      "kernel_id": "25367067"
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    {
      "ref": "rickykonwar/h-m-lightfm-nofeatures-hyperparamter-tuning",
      "title": "H&M_Lightfm_NoFeatures_Hyperparamter_Tuning",
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      "kernel_id": "25365963"
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    {
      "ref": "CVxTz/image-and-text-embeddings-resnet-transformers",
      "title": "Image and Text Embeddings [Resnet+Transformers]",
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      "kernel_id": "25355785"
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    {
      "ref": "andradaolteanu/h-m-eda-rapids-and-similarity-recommenders",
      "title": "👝H&M: EDA, RAPIDS and Similarity Recommenders",
      "source": "live",
      "kernel_id": "25351530"
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      "ref": "harshitvish/ds-eval1",
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      "source": "live",
      "kernel_id": "25351466"
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    {
      "ref": "pixyz0130/h-m-make-submission",
      "title": "H&M make submission 実況",
      "source": "live",
      "kernel_id": "25346374"
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    {
      "ref": "markuslill/h-m-data-preparation",
      "title": "H&M - Data Preparation",
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      "kernel_id": "25345330"
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    {
      "ref": "gbalachandhiran/customer-article-and-transaction-level-eda",
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      "kernel_id": "25340162"
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    {
      "ref": "nancysood/h-m-personalized-fashion-recommendations",
      "title": "H&M Personalized Fashion Recommendations",
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      "kernel_id": "25337580"
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    {
      "ref": "mohammedobeidat/h-m-predicting-next-item-with-decision-tree",
      "title": "H&M: Predicting Next Item with Decision Tree",
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      "kernel_id": "25336721"
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    {
      "ref": "atulverma/h-m-ensembling-with-lstm",
      "title": "H&M Ensembling - with LSTM",
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      "kernel_id": "25334845"
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    {
      "ref": "zhaoxf7/ctr-model-gbdt-lr",
      "title": "CTR model GBDT - LR",
      "source": "live",
      "kernel_id": "25333337"
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    {
      "ref": "eduardocatarino/customer-eda-age-distributions-vs-other-variable",
      "title": "Customer EDA - Age Distributions vs Other Variable",
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      "kernel_id": "25331386"
    },
    {
      "ref": "tbierhance/article-images",
      "title": "Article Images",
      "source": "live",
      "kernel_id": "25329905"
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    {
      "ref": "zhaoxf7/hnm-eda-python-project",
      "title": "HnM EDA Python project",
      "source": "live",
      "kernel_id": "25327373"
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    {
      "ref": "eduardocatarino/articles-eda-providing-several-insights",
      "title": "Articles EDA - Providing several insights ",
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      "kernel_id": "25320323"
    },
    {
      "ref": "rickykonwar/h-m-lightfm-5articlefeatures",
      "title": "H&M_Lightfm_5ArticleFeatures",
      "source": "live",
      "kernel_id": "25318439"
    },
    {
      "ref": "astrung/eda-extract-user-metadata-to-apply-deep-model",
      "title": "EDA: extract user metadata to apply deep model",
      "source": "live",
      "kernel_id": "25311905"
    },
    {
      "ref": "kahtan/comp-h-m",
      "title": "comp h&m",
      "source": "live",
      "kernel_id": "25302120"
    },
    {
      "ref": "sawsanshakir/exploratory-data-analysis-h-m-fashion",
      "title": "Exploratory Data Analysis-H&M-fashion",
      "source": "live",
      "kernel_id": "25289854"
    },
    {
      "ref": "dariussingh/h-m-recommendation-system-v1",
      "title": "H&M_recommendation_system_v1",
      "source": "live",
      "kernel_id": "25272396"
    },
    {
      "ref": "johnnync13/factorization",
      "title": "Factorization",
      "source": "live",
      "kernel_id": "25257388"
    },
    {
      "ref": "pavithra23/eda-for-h-m-data-1",
      "title": "EDA for H&M Data_1",
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      "kernel_id": "25250590"
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    {
      "ref": "maniklakherwal/let-s-do-some-eda",
      "title": "Let's do some EDA",
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      "kernel_id": "25246277"
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    {
      "ref": "sgrimmr/dropping-idea-80-data-and-50-columns-drop",
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      "kernel_id": "25245277"
    },
    {
      "ref": "cyrillblache/h-m-create-candidate-articles",
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      "source": "live",
      "kernel_id": "25238399"
    },
    {
      "ref": "pyy0715/let-s-predict-the-articles-with-the-will-be-sold",
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      "kernel_id": "25235961"
    },
    {
      "ref": "pixyz0130/h-m-items-of-other-customers-buy",
      "title": "H&M items of other customers buy 実況",
      "source": "live",
      "kernel_id": "25230694"
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    {
      "ref": "sussudharsan/h-m-eda-understanding-the-data",
      "title": "H&M EDA - Understanding the Data",
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      "kernel_id": "25221293"
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    {
      "ref": "labbaawwabi/h-m-project-by-awwabi",
      "title": "H&M Project by Awwabi",
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      "kernel_id": "25216039"
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      "ref": "titericz/h-m-ensembling-how-to",
      "title": "H&M Ensembling - How to",
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      "kernel_id": "25213108"
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    {
      "ref": "neesha12/h-m-data-exploration-and-prediction",
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      "kernel_id": "25198260"
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    {
      "ref": "ravimandliya/h-m-eda-first-look",
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      "kernel_id": "25191383"
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      "ref": "abhishekraikaushik/h-and-m",
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      "kernel_id": "25186487"
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      "ref": "leesungreong/eda-detailed-article-text-mining",
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      "kernel_id": "25184256"
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    {
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      "kernel_id": "25179159"
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      "title": "EDA - H&M Data",
      "source": "live",
      "kernel_id": "25173951"
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    {
      "ref": "arunkumar1809/hm-recommender",
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      "kernel_id": "25171835"
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    {
      "ref": "lunapandachan/h-m-byfone-s-speed-up",
      "title": "H&M  Byfone's speed up 実況",
      "source": "live",
      "kernel_id": "25159249"
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    {
      "ref": "yukou00takahashi/h-m-0-0226-faster-version",
      "title": "[H&M] [0.0226] Faster version",
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      "kernel_id": "25159063"
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    {
      "ref": "ebn7amdi/trending",
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      "source": "live",
      "kernel_id": "25155467"
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      "ref": "astrung/eda-extract-campaign-from-transactions",
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      "source": "live",
      "kernel_id": "25154881"
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    {
      "ref": "astrung/lstm-sequential-modelwith-item-features-tutorial",
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    {
      "ref": "leesungreong/eda-customer-article-transaction-special-customer",
      "title": "EDA: Customer,Article,Transaction,Special Customer",
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      "kernel_id": "25150012"
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    {
      "ref": "hervind/h-m-faster-trending-products-weekly",
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      "kernel_id": "25148845"
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    {
      "ref": "rayanaay/h-m-resnet18-encoding-within-your-reach",
      "title": "H&M - Resnet18 Encoding within-your-reach ✅",
      "source": "live",
      "kernel_id": "25145282"
    },
    {
      "ref": "monikachivate/h-m-personalized-fashion-recommendations",
      "title": "H&M Personalized Fashion Recommendations",
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      "kernel_id": "25143274"
    },
    {
      "ref": "infinator/h-m-fashion-visualization-with-plotly",
      "title": " 👚 H&M Fashion | Visualization with Plotly ❤️️ ",
      "source": "live",
      "kernel_id": "25142177"
    },
    {
      "ref": "amanabdullayev/hot-products-of-interested-and-prior-week",
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      "kernel_id": "25141511"
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    {
      "ref": "ratthachat/h-m-customer-compact-summary-micro-eda",
      "title": "H&M - Customer Compact Summary (Micro EDA)",
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      "kernel_id": "25135792"
    },
    {
      "ref": "amanabdullayev/warning-sales-spike-at-end-of-september",
      "title": "!!! WARNING!!! Sales spike at end of September",
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      "kernel_id": "25126990"
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    {
      "ref": "simstar12/tabtranformer",
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      "kernel_id": "25126917"
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    {
      "ref": "yujihirano/h-and-m-kaggle-article-seasonality",
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      "kernel_id": "25124774"
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    {
      "ref": "nuriacami/hym-recommenders-notebook",
      "title": "HyM Recommenders Notebook",
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      "kernel_id": "25124508"
    },
    {
      "ref": "arturxarles/task1-enricazuara-arturxarles",
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      "kernel_id": "25123709"
    },
    {
      "ref": "ikerhonorato/h-m-collaborative-np",
      "title": "H&M, collaborative + NP",
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      "kernel_id": "25121379"
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      "ref": "luisrodri97/item-based-collaborative-filtering",
      "title": "Item-Based Collaborative Filtering",
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      "kernel_id": "25120677"
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    {
      "ref": "noahjadallah/final-notebook",
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      "kernel_id": "25120198"
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      "ref": "tisonludovic/notebook69507d681f",
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      "kernel_id": "25119464"
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      "ref": "byfone/h-m-purchases-in-a-row",
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      "kernel_id": "25116944"
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    {
      "ref": "salmaneunus/movie-eda-recommendation-system",
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      "kernel_id": "25114320"
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    {
      "ref": "kentawatanabetotosya/h-m-eda",
      "title": "H&M_EDA_日本語",
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      "kernel_id": "25114277"
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    {
      "ref": "rickykonwar/h-m-lightfm-nofeatures",
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      "source": "live",
      "kernel_id": "25110014"
    },
    {
      "ref": "hongeunlee/h-m-fashion-eda",
      "title": "H&M fashion EDA ",
      "source": "live",
      "kernel_id": "25107154"
    },
    {
      "ref": "datark1/detailed-eda-understanding-h-m-data",
      "title": "Detailed EDA - Understanding H&M data",
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      "kernel_id": "25101591"
    },
    {
      "ref": "aussie84/clustering-based-on-image-similarity-vs-categories",
      "title": "Clustering based on Image Similarity vs Categories",
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      "kernel_id": "25091275"
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    {
      "ref": "lichtlab/0-0226-byfone-chris-combination-approach",
      "title": "🕊[0.0226]Byfone&Chris combination approach",
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      "kernel_id": "25086124"
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      "ref": "mohammedobeidat/content-based-filtering-with-pca",
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      "kernel_id": "25085072"
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    {
      "ref": "quinnwadas/match-em",
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      "source": "live",
      "kernel_id": "25081956"
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    {
      "ref": "jessezhouu/h-m-predictions",
      "title": "H&M Predictions",
      "source": "live",
      "kernel_id": "25075804"
    },
    {
      "ref": "gbalachandhiran/eda-classes-of-rich",
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      "kernel_id": "25074588"
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    {
      "ref": "tbierhance/basic-data-prep-memory-performance-saver",
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      "kernel_id": "25074251"
    },
    {
      "ref": "salmaneunus/h-m-personalized-recommendation-simple-eda",
      "title": "H&M Personalized Recommendation Simple EDA",
      "source": "live",
      "kernel_id": "25072961"
    },
    {
      "ref": "yunlinlew/h-m-online-store-sales-customer-analysis",
      "title": "H&M Online Store Sales & Customer Analysis",
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      "kernel_id": "25060263"
    },
    {
      "ref": "rickykonwar/h-m-exploratorydataanalysis",
      "title": "H&M_ExploratoryDataAnalysis",
      "source": "live",
      "kernel_id": "25048834"
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    {
      "ref": "tarique7/hnm-exponential-decay-with-alternate-items",
      "title": "HnM Exponential Decay with Alternate Items",
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      "kernel_id": "25042906"
    },
    {
      "ref": "nadianizam/h-m-fashion-eda",
      "title": "👗🥼H & M Fashion(EDA)👗🥼",
      "source": "live",
      "kernel_id": "25041773"
    },
    {
      "ref": "khushbuagrawal/h-m-data-analysis",
      "title": "H&M data Analysis",
      "source": "live",
      "kernel_id": "25040973"
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    {
      "ref": "varshapalatse/h-m-eda-getting-started",
      "title": "H&M EDA Getting Started",
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      "kernel_id": "25039225"
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    {
      "ref": "vladislavnikolov/item-based-recommender",
      "title": "Item-Based Recommender",
      "source": "live",
      "kernel_id": "25023255"
    },
    {
      "ref": "astrung/recbole-lstm-sequential-for-recomendation-tutorial",
      "title": "Recbole:LSTM/sequential for recomendation tutorial",
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      "kernel_id": "24997703"
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    {
      "ref": "alexvishnevskiy/gbm-ranking",
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      "source": "live",
      "kernel_id": "24993146"
    },
    {
      "ref": "jasonbian/apriori-and-association-rules",
      "title": "Apriori and Association Rules",
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      "kernel_id": "24992510"
    },
    {
      "ref": "mirenaborisova/h-m-eda-01",
      "title": "H&M EDA_01",
      "source": "live",
      "kernel_id": "24990437"
    },
    {
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      "kernel_id": "24619771"
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    {
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      "kernel_id": "24612400"
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      "kernel_id": "24610891"
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    {
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      "kernel_id": "24599370"
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