An Improved Profile-based CF Scheme with Privacy

Traditional collaborative filtering (CF) systems widely employing k- nearest neighbor (kNN) algorithms mostly attempt to alleviate the contemporary problem of information overload by generating personalized predictions for items that users might like. Unlike their popularity and extensive usage, they suffer from several problems. First, with increasing number of users and/ or items, scalability becomes a challenge. Second, as the number of ratable items increases and number of ratings provided by each individual remains as a tiny fraction, CF systems suffer from sparsity problem. Third, many schemes fail to protect private data referred to as privacy problem. Due to such problems, accuracy and online performance become worse.