{
  "id": 321104,
  "title": "Recommendation Engine",
  "url": "/competitions/h-and-m-personalized-fashion-recommendations/discussion/321104",
  "author_name": "",
  "post_date": "2022-04-25T03:20:17.361326900Z",
  "votes": null,
  "comment_count": 1,
  "views": 0,
  "content": "<p>Most of kaggler in this competition using recommendation engine for the next 7 days customer purchased items. I believe this is what we have to solve the problem from comeptition data section</p>\n<p>\"<strong>Your task is to predict the article_ids each customer will purchase during the 7-day period immediately after the training data period.</strong>\"</p>\n<p>Any recommendation engine requires some current articles_id , based on these supplied article_id it will find similiarity between items and return back recommended article_id to buy.</p>\n<p>What criteria you are using to supply current article_id to recommendation engine? Is it just random article_ids or current purchased article_ids or something else?</p>",
  "messages": [
    {
      "id": "1767044",
      "postDate": "04/25/2022 03:20:17",
      "content": "<p>Most of kaggler in this competition using recommendation engine for the next 7 days customer purchased items. I believe this is what we have to solve the problem from comeptition data section</p>\n<p>\"<strong>Your task is to predict the article_ids each customer will purchase during the 7-day period immediately after the training data period.</strong>\"</p>\n<p>Any recommendation engine requires some current articles_id , based on these supplied article_id it will find similiarity between items and return back recommended article_id to buy.</p>\n<p>What criteria you are using to supply current article_id to recommendation engine? Is it just random article_ids or current purchased article_ids or something else?</p>",
      "rawMarkdown": "Most of kaggler in this competition using recommendation engine for the next 7 days customer purchased items. I believe this is what we have to solve the problem from comeptition data section\n\n\"**Your task is to predict the article_ids each customer will purchase during the 7-day period immediately after the training data period.**\"\n\nAny recommendation engine requires some current articles_id , based on these supplied article_id it will find similiarity between items and return back recommended article_id to buy.\n\nWhat criteria you are using to supply current article_id to recommendation engine? Is it just random article_ids or current purchased article_ids or something else?",
      "votes": null
    },
    {
      "id": "1771949",
      "postDate": "04/29/2022 17:09:16",
      "content": "<p>You can use data from the past, like ranking of the article the previous week, last purchase etc… This notebook might be a good example of what you are after: <a href=\"https://www.kaggle.com/code/marcogorelli/radek-s-lgbmranker-starter-pack\" target=\"_blank\">https://www.kaggle.com/code/marcogorelli/radek-s-lgbmranker-starter-pack</a></p>",
      "rawMarkdown": "You can use data from the past, like ranking of the article the previous week, last purchase etc... This notebook might be a good example of what you are after: https://www.kaggle.com/code/marcogorelli/radek-s-lgbmranker-starter-pack",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1771949,
      "author_name": "loicge",
      "author_url": "",
      "post_date": "04/29/2022 17:09:16",
      "content": "<p>You can use data from the past, like ranking of the article the previous week, last purchase etc… This notebook might be a good example of what you are after: <a href=\"https://www.kaggle.com/code/marcogorelli/radek-s-lgbmranker-starter-pack\" target=\"_blank\">https://www.kaggle.com/code/marcogorelli/radek-s-lgbmranker-starter-pack</a></p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "1767044": "Most of kaggler in this competition using recommendation engine for the next 7 days customer purchased items. I believe this is what we have to solve the problem from comeptition data section\n\n\"**Your task is to predict the article_ids each customer will purchase during the 7-day period immediately after the training data period.**\"\n\nAny recommendation engine requires some current articles_id , based on these supplied article_id it will find similiarity between items and return back recommended article_id to buy.\n\nWhat criteria you are using to supply current article_id to recommendation engine? Is it just random article_ids or current purchased article_ids or something else?",
    "1771949": "You can use data from the past, like ranking of the article the previous week, last purchase etc... This notebook might be a good example of what you are after: https://www.kaggle.com/code/marcogorelli/radek-s-lgbmranker-starter-pack"
  },
  "source": "meta"
}