Recommendation Engine Achieves 85% Success
April 24, 2008 – 12:10 amAs we’ve been able to gather more rating data from our site, MyFriendSuggests.com we’ve been able to run more tests on our unique recommendation algorithm. Our algorithm uniques combines both user based and item based collaborative filtering. Our recent tweaks to the algorithm have shown improvements and we are now able to achieve better than 85% accuracy on our predictions*. We still believe the algorithm can be improved further as we gain more data.
We believe our algorithm is unique in that it combines both user and item based filtering allowing us to build correlations between how similar two places are (in terms of the people who rate them) and how similar people are to each other. We use both of these factors together (in our unique algorithm) to make our recommendations.
Another great side effect of our approach is the ability for us to use our user correlations to help indicate to our members which other members share similar tastes. This allows them to find out which reviews they should consider when reviewing a location. This is a unique distinguisher for MyFriendSuggests.com because unlike the many other local search sites we can help you find the reviews that will help our users most instead of them reading 100’s of contradicting reviews from people who may not share their tastes.
We hope to continue to improve our recommendation algorithm and we’ll keep updating our blog with any other advancements.
* Our testing methodology was done using root mean squared analysis of our predictions versus actual ratings using the Taste API’s evaluator infrastructure.
collaborative filtering MyFriendSuggests.com Recommendation Engine suggestions
