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Recommender Systems

Recommender Systems produce a list of recommendations in one of two ways:

• Collaborative and Content Based Filtering
• Personality Based Approach

Collaborative Filtering approaches building the model based on user's past behavior (previously purchased items and/or numerical ratings given to items) as well other similar decisions made by users. The model is then used to predict items the user may have an interest in.

Why is this Important?

Recommender Systems are important because:

• They are used to improve customer experience on a variety of websites. Recommender Systems allow the site to primarily show results that are based on past browsing or purchasing history.
• They help customers find products they may enjoy as well as limiting their exposure to products they don't want to see.
• Large technology giants such as Amazon, Netflix, and Spotify rely heavily on Recommender Systems for revenue and drive because they are able to deliver actual value to their customers using accurate recommendations of products/services.