Recommender Systems Complete Course Beginner to Advanced - Basics of Recommender System: Item Model and Memory-Based Col

The video tutorial explains collaborative filtering, a recommendation system technique. It covers item-based filtering, which matches items to similar items, and model-based filtering, which uses user ratings and other data to develop a recommendation model. Memory-based filtering utilizes user-item data for predictions, employing statistical methods like nearest neighbour. The tutorial concludes with a recap of these filtering types, highlighting their differences and applications.

What is the primary focus of item-based collaborative filtering?

• Utilizing the entire user-item data

• Developing a model from user ratings

• Focusing on the items or products

• Matching users to similar customers

Which of the following is a key characteristic of model-based collaborative filtering?

• It uses implicit information like user habits

• It focuses solely on explicit user ratings

• It requires minimal resources

• It is the simplest form of collaborative filtering

What is a major challenge associated with model-based collaborative filtering?

• Lack of user data

• High resource requirements

• Inability to handle large datasets

• Limited to explicit user ratings

Memory-based collaborative filtering primarily relies on which of the following?

• Using minimal memory resources

• Utilizing statistical methods

• Focusing on item similarities

• Developing a predictive model

What is a common method used in memory-based collaborative filtering?

• Classification

• Clustering

• Nearest neighbor

• Rule-based approach

Which type of collaborative filtering is considered the most complex?

• User-based

• Memory-based

• Item-based

• Model-based

Which collaborative filtering approach is important when other data is involved?

• Memory-based

• Model-based

• Item-based

• User-based