Recommendation Systems

The video discusses the importance of recommendation systems for online stores, using examples like Amazon, Netflix, and Spotify. It highlights the challenges online stores face without recommendations, such as lack of personalized experiences and product discovery issues. The video explains how recommendation systems help users find products and increase store engagement. It also touches on the role of data and big data in powering these systems and introduces the concept of building recommendation engines, which will be explored further in the next video.

What is a key reason online platforms like Amazon and Netflix use recommendation systems?

• To limit user access to certain products

• To reduce the number of products available

• To increase the cost of products

• To provide a personalized user experience

What is a major challenge for online stores compared to offline stores?

• Higher operational costs

• Absence of a salesperson for personalized service

• Limited product variety

• Lack of a physical location

How do recommendation systems help online stores with product discoverability?

• By making a wide range of products easily discoverable

• By removing less popular items from the catalog

• By highlighting only the most expensive items

• By reducing the number of products

What type of data do online stores collect to build recommendation systems?

• User activity data including clicks and searches

• Only user ratings

• Only purchase history

• Data from physical store visits

Why is big data important for recommendation systems?

• It reduces the need for data storage

• It allows handling of large-scale data effectively

• It simplifies the recommendation algorithms

• It eliminates the need for user data