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Recommender Systems
  • Level Professional
  • Course by Sungkyunkwan University
  • Offered by

About

A Recommender System is a process that seeks to predict user preferences. This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and dimension reduction techniques for the user-product preference space. This Specialization is designed to serve both the data mining expert who would want to implement techniques like collaborative filtering in their job, as well as the data literate marketing professional, who would want to gain more familiarity with these topics. The courses offer interactive, spreadsheet-based exercises to master different algorithms, along with an honors track where you can go into greater depth using the LensKit open source toolkit. By the end of this Specialization, you’ll be able to implement as well as evaluate recommender systems. The Capstone Project brings together the course material with a realistic recommender design and analysis project.

Modules

What is a recommender system?

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1 Assignment

  • Quiz 1

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  • Main recommender systems

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  • Recommender system application developments: A survey

Basic Recommender Systems

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  • Quiz 2

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  • Read data, measure accuracy

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  • The Limits of Popularity-Based Recommendations, and the Role of Social Ties

Collaborative Filtering I

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  • Quiz 3

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  • Principles of Collaborative Filtering

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  • Time Weight Collaborative Filtering

Collaborative Filtering II

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  • Quiz 4

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  • User-Based CF vs. Item-Based CF

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  • Setting Goals and Choosing Metrics for Recommender System Evaluations

Matrix Factorization

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  • Quiz 5

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  • Principles of matrix factorization

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  • Matrix factorization techniques for recommender systems

Recommander System based on Matrix Factorization

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  • Quiz 6

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  • Matrix factorization algorithm

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  • Matrix Factorization and Recommender Systems

Surprise Package

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  • Quiz 7

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  • Introduction to Surprise package

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  • Surprise: A Python library for recommender systems

Recommender systems using deep learning 1

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  • Quiz 8

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  • Compare Algorithms and Set Options

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  • Convolutional Matrix Factorization for Document Context-Aware Recommendation

Recommender systems using deep learning 2

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  • Quiz 9

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  • Deep Learning Recommendation using Keras 2

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  • Deep Learning Based Recommender System: A Survey and New Perspectives

Hybrid Recommender Systems

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1 Assignment

  • Quiz 10

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  • Combination of CF and MF

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  • Matrix factorization model in collaborative filtering algorithms: A survey

Sparse Matrix

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  • Quiz 11

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  • Processing of large-scale data

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  • A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method

Some Issues in RS

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    • 2 Assignment
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2 Assignment

  • Quiz 12
  • Final Test

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  • Cold start, scalability, binary data

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  • DeepFM: a factorization-machine based neural network for CTR prediction

Auto Summary

Unlock the power of predicting user preferences with the "Recommender Systems" course. Dive into non-personalized, content-based, and collaborative filtering techniques, and explore advanced topics like matrix factorization and hybrid machine learning methods. Ideal for data mining experts and marketing professionals, this professional-level course from Coursera includes interactive exercises and an honors track with LensKit. Conclude with a Capstone Project to apply your knowledge. Available via a Starter subscription.

Jaekwang KIM
Instructor

Jaekwang KIM