This (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&#x27;t know what algorithms it uses.<p>I developed an online recommendation system (see <a href="http:&#x2F;&#x2F;web.onetel.com&#x2F;~hibou&#x2F;morse&#x2F;MORSE.html" rel="nofollow">http:&#x2F;&#x2F;web.onetel.com&#x2F;~hibou&#x2F;morse&#x2F;MORSE.html</a> 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&#x27;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.<p>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.