removing redundancy and inconsistency in memory-based collaborative filtering
The application range of memory-based collaborative filtering (CF) is limited due to CF's high memory consumption and long runtime. The approach presented in this paper removes redundant and inconsistent instances (users) from the data. This paper aims to distinguish informative instances (users) from large raw user preference database and thus alleviate the memory and runtime cost of the widely used memory-based collaborative filtering (CF) algorithm. Our work shows that a satisfactory accuracy can be achieved by using only a small portion of the original data set, thereby alleviating the storage and runtime cost of the CF algorithm. In our approach, we consider instance selection as the problem of selecting informative data that increase the We begin by discussing the instance selection problem in a general sense that is to increase a posteriori probability of the optimal model by selecting informative data. We evaluate the empirical performance of our approach PF on two real-world data sets and attain very promisingpositive experimental results. The dData size and the prediction time are significantly reduced, while the prediction accuracy is on a par with almost the same as the results achieved by using the complete database.
collaborative filtering data selection efficiency scalability