Experimental Results on Item-Based Algorithms for Independent Domain Collaborative Filtering

A research analysis on item-based algorithms for collaborative filtering is presented. The aim of the presented activity was to find a configuration of an item-based algorithm capable of providing good results but also independent from the data set. Four data sets were used for the algorithm validation: Netflix, MovieLens, BookCrossing, and Jester. The experimentation involved the following aspects: similarity computation, size of the neighbourhood, prediction computation, minimum number of co-rated items. Results were evaluated in terms of root mean squared error (RMSE). The result of the activity is an independent domain configuration for an item-based algorithm which produced satisfactory results with most of the above mentioned data sets.