Glad to hear that someone has still found a need for expert systems.
Am I correct in assuming Lazyday has adopted a rule-based approach, using information about the movies (e.g. release date, genre, director, cast, etc.) and users (e.g. age, sex, nationality, current mood, etc.) that collaborative filtering systems don't use, and that you have evaluated the system against a collaborative filtering system which uses the ratings your users provided?
I developed an online collaborative filtering movie recommendation system in the mid 1990s ( http://www.fmjlang.co.uk/morse/MORSE.html ), and analysed its performance, for my MSc project. The only information it used was how each user rated the movies they had seen. It used no other information about the movies or the users themselves.
A surprising result I found was that using the ratings of the person whose tastes most closely matches your own to predict your ratings was very inaccurate. (Note that this is not necessarily the same as someone recommending you a movie, as they might know and take into account your tastes, and even if they didn't, they could still improve their recommendations simply by recommending the most popular movies, rather than the movies they liked.) Expanding from a single nearest neighbour to the average of the N nearest neighbours (for N = 25) gave quite good results. It seems that you're more similar to an average of all your friends than your closest friend. Using the algorithm I developed (described in detail in the paper) resulted in a further big improvement, which was unsurpassed until the Netflix prize. My data set was significantly smaller, though.
Yes Lazyday is a rules based search engine. It makes best guesses based on what's currently popuplar, the movie's meta data and user search preferences and history. We have not evaluated it against a CF system in an A/B setup. Of course we have years of user frustration with Netflix / Prime recommends that has taught us a few valuable lessons about how to solve the problem of what to watch next.
And yes it seems intuitive to assume that two people with the same tastes will like the same titles. The problem is, I might not be in the mood for movie A even though my virtual twin is, sometimes I would be in the mood for movie A if I had company that was also in the mood for movie A. So many edge cases when a recommendation engine is trying to guess what you want to watch next.
Using the averages of your friends is very interesting. The issue would be building your dataset. Attaching it to a network like FB or building it yourself which would take even more time.
Am I correct in assuming Lazyday has adopted a rule-based approach, using information about the movies (e.g. release date, genre, director, cast, etc.) and users (e.g. age, sex, nationality, current mood, etc.) that collaborative filtering systems don't use, and that you have evaluated the system against a collaborative filtering system which uses the ratings your users provided?
I developed an online collaborative filtering movie recommendation system in the mid 1990s ( http://www.fmjlang.co.uk/morse/MORSE.html ), and analysed its performance, for my MSc project. The only information it used was how each user rated the movies they had seen. It used no other information about the movies or the users themselves.
A surprising result I found was that using the ratings of the person whose tastes most closely matches your own to predict your ratings was very inaccurate. (Note that this is not necessarily the same as someone recommending you a movie, as they might know and take into account your tastes, and even if they didn't, they could still improve their recommendations simply by recommending the most popular movies, rather than the movies they liked.) Expanding from a single nearest neighbour to the average of the N nearest neighbours (for N = 25) gave quite good results. It seems that you're more similar to an average of all your friends than your closest friend. Using the algorithm I developed (described in detail in the paper) resulted in a further big improvement, which was unsurpassed until the Netflix prize. My data set was significantly smaller, though.