Is like-i-like really what-i-like?

like-i-like.org is a movie recommendation system that oddly enough recommends movies! lol It learns your movie taste, from ratings provided by the user, and makes personalized predictions specially for you. It offers a feature which allows you to find "movie soulmates", individuals who have similar ratings and preferences. You can even see how many votes your "soulmate" has provided and see how they voted on a particular movie!! These soulmates can also be contacted to ask for their opinion on movies that you have yet to view. According to the program's FAQ on achieving the best (most precise) predictions, users should be objective in their rankings. Predictions appear after you rate at least 10 items, but going over 20 ranked items results in much moer accurate forecast.

So i registered to use the system and after rating 30+ movies, i question whether their recommendations are in fact "what-i-like"? It appears the movie database in still in a building stage because a number of the movies that I searched for to vote on where not found. After rating 6 movies, predictions began to appear on how I would rate the movie. Of the following 6 movies that I rated, 3 of the predictions were accurate (when rounded) and 3 where within 1 or 2 points. So after giving 12 votes, I decided to see what type of recommendations the system would offer. Viewing only the top 10, all of the movies suggested were ranked 9.9 and I have to admit I've never seen or even heard of them :-( Perhaps, this is why they suggest ranking at least 20 movies. So onward I went. I rated another 9 movies and 3 of the predictions were accurate (when rounded) and again the rest were within a point or two. What struck me as odd during this period is I performed a search for Spiderman...to my amazement, the movie didn't appear!! Right or wrong, I question the validity of any system that doesn't have at least the first Spiderman in its database....call me crazy but that's one of the best movies, all time!! So by the end of this phase I've rated a total of 21 songs and wanted to take a look at the recommendations again. The system predicted I would rate each movie a 10 but with the exception of Dirty Harry-which i vaguely remember b/c of the title though I dont remember watching it, none look even remotely familiar. I've never claimed to be a movie connoisseur so I like what I like and on rare occassion, I step outside of that realm and land on something interesting that may have never caught my eye. Even with that said, I guess I was expecting to see more movies that I've seen or heard about and would enjoy watching. I mean I've rated some of my all-time favorites at this point and they should give a pretty good indication of what I like but either I'm the odd ball and other users don't agree with my taste or the system isn't as knowledgeable as I would like. Wanting to go just another step further, I rated 10 more movies. On 2 of the 10, the system didn't offer any predictions at all. 5 of the remaining 8 were accurate and the last 3 were within a few points of accuracy. Worth noting, the system predicated i would rank Malibu's Most Wanted, which I thought was a very poor movie though I admittedly laughed maybe twice, at least a 5.5 and when rounded is a 6 which is slightly good when i actually gave it a 3. Conversely, Training Day which I think is a phenomenal movie, masterpiece even, was only rated 8.9...I wish I could see the brains of the operation to give it a piece of my mind too! lol So I decided to check out the recommendations again, hoping for the best, and still nothing!! Two of the 10 movies recommended, previously appeared in the list and that lets me know either the system thinks it's right and that I would REALLY REALLY like this movie or the system has not be able to infer anything from my additional ratings :-( I didn't recognize a single title in the entire list....perhaps I just expect too much!! lol

Remi A - signing off!

(BTW - because of the ranking/rating system used, like-i-like uses collaborative filtering to make recommendations)

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