Introduction to Recommendation Systems Training Course
Marketing department employees, IT strategists and other people involved in decisions related to the design and implementation of recommender systems.
Short theoretical background follow by analysing working examples and short, simple exercises.
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
- Determining weights of the search results
- Using Synonyms
- Full-text search
- Chris Anderson idea
- Drawbacks of Long Tail
- Documents and web sites
Content-Based Recommendation i measurement of similarities
- Cosine distance
- The Euclidean distance vectors
- TFIDF and frequency of terms
- Community rating
- Applications of graphs
- Determining similarity of graphs
- Similarity between users
- 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
- Functionality and UX
- Popularity of recommender systems and their problems
Public ClassroomParticipants from multiple organisations. Topics usually cannot be customised
Private ClassroomParticipants are from one organisation only. No external participants are allowed. Usually customised to a specific group, course topics are agreed between the client and the trainer.
Private RemoteThe instructor and the participants are in two different physical locations and communicate via the Internet
The more delegates, the greater the savings per delegate. Table reflects price per delegate and is used for illustration purposes only, actual prices may differ.
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