Building Similarity Based Recommendation System

4.4
27件の評価
提供:
Coursera Project Network
このガイド付きプロジェクトでは、次のことを行います。

Understand what is collaborative filtering and how to collect data to build a recommendation system

Understand how to create user item interactions matrix to find which users are most similar to the other users

Build a similarity based recommendation system based on collaborative filtering

Clock2 hour
Intermediate中級
Cloudダウンロード不要
Video分割画面ビデオ
Comment Dots英語
Laptopデスクトップのみ

Welcome to this 1-hour project-based course on Building Similarity Based Recommendation System. In this project, you will learn how similarity based collaborative filtering recommendation systems work, how you can collect data for building such systems. You will learn what are some different ways you to compute similarity between users and recommend items based on products interacted by other similar users. You will learn to create user item interactions matrix from the original dataset and also how to recommend items to a new user who does not have any historical interactions with the items. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

あなたが開発するスキル

  • Data Manipulation
  • cosine similarity
  • Recommender Systems

ステップバイステップで学習します

ワークエリアを使用した分割画面で再生するビデオでは、講師がこれらの手順を説明します。

  1. Understanding collaborative filtering and dataset

  2. Exploring the dataset

  3. Creating user item interactions matrix

  4. Finding similar users

  5. Creating similarity based recommendation system

  6. Conclusion

ガイド付きプロジェクトの仕組み

ワークスペースは、ブラウザに完全にロードされたクラウドデスクトップですので、ダウンロードは不要です

分割画面のビデオで、講師が手順ごとにガイドします

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