26. Content Based Recommender Systems | Machine Learning with Python | Tech2Teach скачать в хорошем качестве

26. Content Based Recommender Systems | Machine Learning with Python | Tech2Teach

In this video, we’ll be covering content-based recommendation systems. So let’s get started. A content-based recommendation system tries to recommend items to users, based on their profile. The user’s profile revolves around that user’s preferences and tastes. It is shaped based on user ratings, including the number of times that user has clicked on different items or perhaps, even liked those items. The recommendation process is based on the similarity between those items. Similarity, or closeness of items, is measured based on the similarity in the content of those items. When we say content, we’re talking about things like the item’s category, tag, genre, and so on. For example, if we have 4 movies, and if the user likes or rates the first 2 items, and if item 3 is similar to item 1, in terms of their genre, the engine will also recommend item 3 to the user. In essence, this is what content-based recommender system engines do. Now, let’s dive into a content-based recommender system to see how it works. Let’s assume we have a dataset of only 6 movies. This dataset shows movies that our user has watched, and also the genre of each of the movies. For example, “Batman versus Superman” is in the Adventure, Super Hero genre. And “Guardians of the Galaxy” is in Comedy, Adventure, Super Hero, and Science-Fiction genres. #MachineLearning​ #PythonMachineLearning​ #MachineLearningTutorial​