{"id":188,"date":"2018-01-11T15:33:00","date_gmt":"2018-01-11T14:33:00","guid":{"rendered":"https:\/\/kindsonthegenius.com\/blog\/2018\/01\/11\/introduction-of-recommender-systems\/"},"modified":"2020-08-22T10:26:54","modified_gmt":"2020-08-22T08:26:54","slug":"introduction-of-recommender-systems","status":"publish","type":"post","link":"https:\/\/kindsonthegenius.com\/blog\/introduction-of-recommender-systems\/","title":{"rendered":"Introduction of Recommender Systems"},"content":{"rendered":"
\n
We would try to clearly explain the concept of a recommender system.<\/div>\n
<\/div>\n
What is a Recommender System<\/b><\/div>\n
A recommender system is a system that is used to make prediction about what the user preference may be on the items before the user does it.<\/p>\n

We are going to briefly discuss the following three topics:<\/p>\n

    \n
  1. Applications of Recommender Systems<\/span><\/li>\n
  2. Formulating the Problem – Utility Matrix<\/span><\/li>\n
  3. Formal Model<\/span><\/li>\n
  4. Final Notes <\/span><\/li>\n<\/ol>\n
      <\/ol>\n

      Applications of Recommender Systems<\/b>
      Recommender systems a used a  wide array of application, it can be arguably the most applied machine learning technology in online systems. Some of the application areas are given below<\/p>\n

        \n
      1. Movie recommendation in Netflix<\/li>\n
      2. Related products recommendation in Amazon<\/li>\n
      3. Web page ranking in Google<\/li>\n
      4. Friends recommendation in social networks eg. Facebook<\/li>\n
      5. News content recommendation in Yahoo News<\/li>\n
      6. Priorities Inbox and Spam mail filtering<\/li>\n
      7. Computational advertising in Yahoo<\/li>\n
      8. Online dating networks eg okCupid<\/li>\n<\/ol>\n

        Kinds of Algorithms used in Recommender Systems<\/p>\n

          \n
        • Content-based Systems<\/li>\n
        • Collaborative filtering<\/li>\n
        • Latent factor based models<\/li>\n<\/ul>\n

          Utility Matrix – Formulating the Problem<\/b>
          An approach to building a recommender system is the use of a utility matrix. This is a table\/matrix that show the values or rating users attach to items they use.
          Let’s take the example of user ratings of movies. Ratings are from 0 to 5 stars. Table illustrates this:<\/p>\n\n\n\n\n
          <\/a><\/td>\n<\/tr>\n
          Table 1: Illustration of the Utility Matrix<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

          From the utility matrix, the challenge of the recommender system is to infer unknown(labeled with ?) ratings from the known ratings<\/p>\n

          Approach to Recommender System<\/b>
          Step 1<\/span>: Gathering the “known” ratings. This data can be collected from the utility matrix.
          Step 2<\/span>: Extrapolate unknown ratings from the known ratings. The focus would be on high unknown ratings so that it can be used to make recommendation
          Step 3:<\/span> Evaluating the Extrapolation methods. This refers to a way of measuring the success\/performance of the recommendation methods.<\/p>\n

            <\/ol>\n

            Formal Model<\/b>
            Using the utility matrix, we can  deduce a formal model for recommender systems using the following assumptions:<\/p>\n

            C = set of customers
            S = set of item (movies in this case)
            Utility function u = C x S \u2192 R
            R = set of ratings
            R is an ordered set eg 0 -5, or real numbers  in [0, 1]<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

            We would try to clearly explain the concept of a recommender system. What is a Recommender System A recommender system is a system that is … <\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_mi_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0},"categories":[11,16],"tags":[],"_links":{"self":[{"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/posts\/188"}],"collection":[{"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/comments?post=188"}],"version-history":[{"count":1,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/posts\/188\/revisions"}],"predecessor-version":[{"id":1463,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/posts\/188\/revisions\/1463"}],"wp:attachment":[{"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/media?parent=188"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/categories?post=188"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/kindsonthegenius.com\/blog\/wp-json\/wp\/v2\/tags?post=188"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}