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Maybe of interest to some here is MovieLens[1], a research project from University of Minnesota that makes personalised movie recommendations based on your own
by SyneRyder 11y ago
Maybe of interest to some here is MovieLens[1], a research project from University of Minnesota that makes personalised movie recommendations based on your own ratings. You can dive behind the scenes and switch between different algorithms, compare its predicted score against the average review score, and there's a page with statistics on your own ratings. (Preferred genres, ratings curve, ratio of mainstream to obscure movies, etc.)
After rating 250+ movies on there myself, my own ratings demonstrate the same right-skew graph curve that IMDB and Fandango has in the article.
[1] https://movielens.org https://movielens.org
- DonaldFisk 11y agoThis (collaborative filtering) is a better approach, but it depends very much on the algorithm used, and how many movies you have already rated. I have not subscribed to MovieLens, and don't know what algorithms it uses. I developed an online recommendation system (see http://web.onetel.com/~hibou/morse/MORSE.html http://web.onetel.com/~hibou/morse/MORSE.html for details) about 20 years ago. For a user who had entered sufficient ratings for other movies, the algorithm I used (described in the paper) performed better than the more widely used average of M nearest neighbours, which in turn is better than simply averaging everyone's ratings. All it used were the individual ratings given by its users. It was entirely agnostic as to genre (which is quite fuzzy), cast, or crew. I was aware of a tendency for people to rate movies more highly if they watched them recently, but never asked users for this data. Surprisingly, using the ratings of only the one person whose tastes are closest to your own (i.e. M=1) results in very inaccurate predicted ratings.