Editor’s Note: In the two years I’ve been playing with AI I’ve shared what I thought were some of the more interesting “conversations”, always being careful to distinguish something I wrote from AI output. ChatGPT frequently offers to compose a blog post and the drafts are as well written (better?) than mine. But they’re not mine. ChatGPT wrote (or plagiarized ) the following.

MovieLens is a free, non-commercial movie recommendation website developed by the GroupLens research lab at the University of Minnesota. It’s designed to help users discover movies they’re likely to enjoy based on their individual tastes. The site uses collaborative filtering algorithms to generate personalized recommendations, improving as more ratings are added.

Getting started is simple: users rate movies they’ve already seen using a five-star scale. The more films you rate, the better the system becomes at understanding your preferences. MovieLens then offers tailored suggestions that can be filtered by genre, release decade, popularity, and more.

One of the key strengths of MovieLens is its clean, ad-free interface and its emphasis on transparency. It doesn’t promote content for profit, and it doesn’t track users for advertising purposes. The recommendations are driven purely by user data and academic research.

Users can view their own rating history, create custom movie lists, and explore trends or themes in their viewing habits. While it lacks the social features of some commercial services, its core focus is accuracy and simplicity in recommendation.

MovieLens is especially useful for cinephiles who want to keep a record of what they’ve seen and find new films aligned with their personal taste, without the influence of box office hype or crowd-sourced noise. It’s also a valuable tool for researchers and students studying recommendation systems, as its algorithm and structure have been used in numerous academic projects and publications.