{"id":2034,"date":"2021-06-03T16:33:48","date_gmt":"2021-06-03T09:33:48","guid":{"rendered":"https:\/\/arikuncoro.xyz\/blog\/?p=2034"},"modified":"2022-03-01T07:22:11","modified_gmt":"2022-03-01T00:22:11","slug":"unravel-the-big-steps-of-tailored-recommender-systems","status":"publish","type":"post","link":"https:\/\/arikuncoro.xyz\/blog\/data-science\/unravel-the-big-steps-of-tailored-recommender-systems\/","title":{"rendered":"UNRAVEL THE BIG STEPS OF TAILORED RECOMMENDER SYSTEMS"},"content":{"rendered":"\n

The 65th writing challenge <\/p>\n\n\n\n

By Bernardus Ari Kuncoro <\/p>\n\n\n\n

Let’s carry on the yesterday’s article<\/a>. There are three main steps for creating a tailored recommender system \/ recommendation engine \/ product recommendation. We call it P-E-E. Prepare, Estimate, and Evaluate. <\/p>\n\n\n\n

  1. Prepare<\/strong>. You should collect and format the ‘rating’ data towards the product we consumed. In the market place the rating data can be derived by successful transactions OR the star rating towards the product that we bought. In Spotify and YouTube, your listening and watching history will be useful respectively. <\/li>
  2. Estimate<\/strong>. You will find the similarity between the users and products with several techniques. You can choose collaborative filtering, content-based, and hybrid. Then predict the rating by calculating the weighting average. <\/li>
  3. Evaluate.<\/strong> This step is done by splitting training and testing and minimising RMSE. <\/li><\/ol>\n\n\n\n

    By doing those steps (at least until step-2), you can at least finish the following problem. <\/p>\n\n\n\n

    \"\"<\/a>
    Predict the product 1 rating inputted by customer 5! Source here<\/a>. <\/figcaption><\/figure>\n\n\n\n

    Kalideres, 2 June 2021<\/p>\n","protected":false},"excerpt":{"rendered":"

    The 65th writing challenge By Bernardus Ari Kuncoro Let’s carry on the yesterday’s article. There are three main steps for …<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[118],"tags":[764,765,762,766],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/arikuncoro.xyz\/blog\/wp-json\/wp\/v2\/posts\/2034"}],"collection":[{"href":"https:\/\/arikuncoro.xyz\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/arikuncoro.xyz\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/arikuncoro.xyz\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/arikuncoro.xyz\/blog\/wp-json\/wp\/v2\/comments?post=2034"}],"version-history":[{"count":3,"href":"https:\/\/arikuncoro.xyz\/blog\/wp-json\/wp\/v2\/posts\/2034\/revisions"}],"predecessor-version":[{"id":3310,"href":"https:\/\/arikuncoro.xyz\/blog\/wp-json\/wp\/v2\/posts\/2034\/revisions\/3310"}],"wp:attachment":[{"href":"https:\/\/arikuncoro.xyz\/blog\/wp-json\/wp\/v2\/media?parent=2034"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/arikuncoro.xyz\/blog\/wp-json\/wp\/v2\/categories?post=2034"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/arikuncoro.xyz\/blog\/wp-json\/wp\/v2\/tags?post=2034"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}