Introduction to Recommendation Systems Training Course

Description

• Type

  Course

• Methodology

  Online

Description

Audience
Marketing department employees, IT strategists and other people involved in decisions related to the design and implementation of recommender systems.
Format
Short theoretical background follow by analysing working examples and short, simple exercises.

About this course

Requirements

Basic knowledge of e-commerce platforms (can be as a user of these platforms).

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Subjects

• Networks
• Marketing
• Design
• Network Training
• Web
• Data Collection
• Network
• Data Mining
• Systems
• Data Types

Course programme

Challenges related to data collection

• Information overload
• Data types (video, text, structured data, etc...)
• Potential of the data now and in the near future
• Basics of Data Mining

Recommendation and searching

• Searching and Filtering
• Sorting
• Determining weights of the search results
• Using Synonyms
• Full-text search

Long Tail

• Chris Anderson idea
• Drawbacks of Long Tail

Determining Similarities

• Products
• Users
• Documents and web sites

Content-Based Recommendation i measurement of similarities

• Cosine distance
• The Euclidean distance vectors
• TFIDF and frequency of terms

Collaborative filtering

• Community rating

Graphs

• Applications of graphs
• Determining similarity of graphs
• Similarity between users

Neural Networks

• Basic concepts of Neural Networks
• Training Data and Validation Data
• Neural Network examples in recommender systems

How to encourage users to share their data

• Making systems more comfortable
• Navigation
• Functionality and UX

Case Studies

• Popularity of recommender systems and their problems
• Examples

Additional information

7 hours (usually 1 day including breaks)

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