What is the difference between explicit and implicit feedback in recommender systems?

In recommender systems, explicit and implicit feedback are two different types of information that users provide to indicate their preferences or interests.

Explicit feedback refers to direct and intentional feedback given by users, where they explicitly express their opinions or ratings on items. This can include explicit ratings, reviews, or explicit likes/dislikes. For example, a user giving a movie a rating of 4 stars or writing a review about a product.

On the other hand, implicit feedback is derived from user behavior or actions that are not explicitly provided as feedback. It is inferred from the user's interactions, such as their purchase history, browsing patterns, click-through rates, or time spent on certain items. Implicit feedback is more indirect and requires algorithms to interpret user behavior to understand their preferences. For instance, if a user frequently clicks on articles related to technology, it can be inferred that they have an interest in technology-related items.

The main difference between explicit and implicit feedback lies in the way they are collected and interpreted. Explicit feedback is more straightforward and requires users to actively provide their opinions, while implicit feedback is derived from user behavior and requires algorithms to analyze and interpret the data.

Both types of feedback have their advantages and limitations. Explicit feedback provides clear and direct information about user preferences, but it can be limited by the number of users willing to provide explicit ratings or reviews. Implicit feedback, on the other hand, can capture a larger amount of user data without requiring explicit actions, but it may be more challenging to interpret and may not always accurately reflect user preferences.

Recommender systems often combine both explicit and implicit feedback to improve the accuracy and effectiveness of recommendations. By considering both types of feedback, these systems can provide more personalized and relevant recommendations to users.