Recommender system algorithms

Abstract

Algorithms One of the most commonly used algorithms in recommender systems is the k-nearest neighborhood approach.6. In a social network, a particular user's neighborhood with similar taste or interest can be found by calculating Pearson Correlation, by collecting the preference data of top-N nearest neighbors of the particular user (weighted by similarity), the user's preference can be predicted by calculating the data using certain techniques. Another family of algorithms that is widely used in recommender systems is collaborative filtering. One of the most common types of Collaborative Filtering is item-to-item collaborative filtering (people who buy x also buy y), an algorithm popularized by Amazon.com's recommender system. User-based collaborative filtering attempts to model the social process of asking a friend for a recommendation. A particular type of collaborative filtering algorithms uses matrix factorization, a low-rank matrix approximation techique78. A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of accurately recommending complex items such as movies without requiring an "understanding" of the item itself. The Netflix Prize, a contest with a dataset of over 100 million movie ratings and a grand prize of $1,000,000, has energized the search for new and more accurate algorithms. The most accurate algorithm in 2007 used 107 different algorithmic approaches, blended into a single prediction:9 Predictive accuracy is substantially improved when blending multiple predictors. Our experience is that most efforts should be concentrated in deriving substantially different approaches, rather than refining a single technique. Consequently, our solution is an ensemble of many methods.