In today’s digital age, the sheer volume of content available can be overwhelming. Movie recommendation systems aim to simplify the search by suggesting films that align with user preferences. Traditional recommendation systems have served us well, but with advances in neural networks and natural language processing (NLP), we can take these systems to the next level. This article explores how collaborative filtering with neural networks, enhanced by movie descriptions, can provide more accurate and personalized movie recommendations.
Collaborative filtering is a technique used by recommendation systems to predict a user’s preferences by analyzing the preferences of many users. It operates on the idea that if User A and User B have similar tastes, User A might enjoy some of the movies that User B likes, which User A hasn’t seen yet. There are two main types of collaborative filtering:
- User-Based Collaborative Filtering: This method finds users who have rated movies similarly to the target user and recommends movies that these similar users have liked.
- Item-Based Collaborative Filtering: This method identifies movies that are…
