Enrolled: 698 students
Duration: 3 days
Video: Remote / Physical Training
Level: All Level


Working hours

Monday 9:30 am - 6.00 pm
Tuesday 9:30 am - 6.00 pm
Wednesday 9:30 am - 6.00 pm
Thursday 9:30 am - 6.00 pm
Friday 9:30 am - 5.00 pm
Saturday Closed
Sunday Closed
HRD Corp Claimable Recommendation System Training

HRD Corp Claimable Recommendation System Training

What is a system for recommending things?
Recommendation engines are a type of machine learning that usually rank or rate products or users. A recommender system is, in a broad sense, a system that predicts the rating a user might give to a certain item. Then, these predictions will be ranked, and the user will get the results.

They are often used by big companies like Google, Instagram, Spotify, Amazon, Reddit, Netflix, etc. to get users more involved with the platform and with each other. So you can keep listening to music on Spotify, for example, it will suggest songs that are similar to the ones you’ve listened to a lot or liked. Amazon uses recommendations to tell different users about products based on the information they have about that user. People often think of recommendation systems as “black boxes” because the models that these big companies use are hard to figure out. The results that are given to the user are often suggestions for things they need or want but didn’t know they needed or wanted until they were told.

  • Acquire the skills necessary to work with both case-based and constraint-based recommenders.
  • Get yourself acquainted with the monolithic, parallelized, and pipelined hybridization design approaches.
  • Acquire the knowledge necessary to evaluate recommender systems.


Attending this class does not require any prior knowledge or experience.


Anyone who desires to acquire an in-depth understanding of the Recommendation system and wants to attend this course can do so. This class is recommended for those with:

  • Engineers Specializing in Machine Learning
  • Data engineers and Scientists
  • Data Analysts

Overview of the Training Course for the Recommendation System
A recommendation system is a broad category of web applications that involves the process of anticipating how a user will react to the available choices. It is a data filtering technology that analyzes previous data to determine what consumers will be interested in and then generates appropriate suggestions based on those findings. The most common applications for this system are found in content-based services, e-commerce platforms, and social media. The goal of this training on the recommendation system is to provide delegates with the knowledge necessary to become proficient in all of the essential strategies utilized by the recommender system.

Participants in this training on recommendation systems will gain an understanding of the fundamental concepts underlying these systems. The model-based and preprocessing-based methodologies will each be explained in detail to the delegates. Delegates will also learn how to work with constraint and case-based recommenders during this course.

Participants will leave with a comprehensive understanding of hybrid recommendation strategies after attending this one-day session. Delegates will get an understanding of explanations in constraint, case, and collaboratively based recommenders after completing this course. Following successful completion of this course, delegates will have the ability to assess recommender systems.

Outline of the HRD Corp Claimable Recommendation System Training

  • Mission Statement of the Recommender System
  • Challenges Specific to the Domain of Recommender Systems and Their Basic Models
  • Collaborative Recommendation

Recommendations Made Based on Users and Items in the Neighborhood

  • Approaches that are Model-Based as well as Preprocessing-Based
  • Knowledge-Based Recommendation That Is Grounded in Practical Methods and Systems

The Representation of Knowledge and Its Reasoning

  • Having Conversations with Recommenders Driven by Constraints
  • Participating in Discussions with Case-Based Recommenders
  • Hybrid approaches to making recommendations

Possibilities for the Use of Hybridization

  • Monolithic Hybridization Design Parallelized Hybridization Design Pipelined Hybridization Design Recommender Systems Explanations Monolithic Hybridization Design Parallelized Hybridization Design Pipelined Hybridization Design
  • Case-Based Recommenders Recommenders Determined According to Constraints
  • Evaluating Recommender Systems Through the Use of Collaborative Filtering Recommenders

Research on Evaluation Qualities and Traits

  • Commonly Used Formulations For Evaluating
  • An Analysis of Previously Collected Datasets
  • Various Other Designs for the Evaluation


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