Recommender systems are algorithms providing personalized suggestions for items that are most relevant to each user. With the massive growth of available online contents, users have been inundated with choices. It is therefore crucial for web platforms to offer recommendations of items to each user, in order to increase user satisfaction and engagement. The following list shows examples of well-known web platforms with a huge number of available contents, which need efficient recommender systems to keep users interested.

1. Youtube. Every minute people upload 500 hours of videos, i.e. it would take 82 years to a user to watch all videos uploaded just in the last hour.
2. Spotify. Users can listen to ore than 80 million song tracks and podcasts.
3. Amazon. Users can buy more than 350 million different products.

All these platforms use powerful machine learning models in order to generate relevant recommendations for each user.

Explicit Feedback vs. Implicit Feedback

In recommender systems, machine learning models are used to predict the rating rᵤᵢ of a user u on an item i. At inference time, we recommend to each user u the items l having highest predicted rating rᵤᵢ.

We therefore need to collect user feedback, so that we can have a ground truth for training and evaluating our models. An important distinction has to be made here between explicit feedback and implicit feedback.

Explicit feedbackis a rating explicitly given by the user to express their satisfaction with an item. Examples are: number of stars on a scale from 1 to 5 given after buying a product, thumb up/down given after watching a video, etc. This feedback provides detailed information on how much a user liked an item, but it is hard to collect as most users typically don’t write reviews or give explicit ratings for each item they purchase.

Implicit feedback, on the other hand, assume that user-item interactions are an indication of preferences. Examples are: purchases/browsing history of a user, list of songs played by a user, etc. This feedback is extremely abundant, but at the same time it is less detailed and more noisy (e.g. someone may buy a product as a present for someone else). However, this noise becomes negligible when compared to the sheer size of available data of this kind, and most modern Recommender Systems tend to rely on implicit feedback.