earticle

영어

Most recommender systems based on collaborative filtering aim to provide recommendations for a user in one domain. But data sparsity is a major problem for collaborative filtering techniques. Recently, many scholars have proposed recommendation models to alleviate the sparsity problem by transferring rating matrix in other domains. But different domains have different rating scales (e.g., rating scale may be 1-5 or 1-10). Simple process for the rating scale does not reflect the real situation. The diversity of rating scales may cause the opposite effect, making the recommendation results more imprecise. In this paper, we propose a transfer model which learning the common feature tags from other domain. This model ignores the difference of rating scales between two domains, and focus on studying the feature tags. Using its own rating values to fill the missing value. We first get the different types of users (items) based on non-negative matrix tri-factorization from auxiliary domain. The process we call the user (item) clustering. Than we can get a BP neural network which can judge the type of user according to user's feature tags by studying the features of different types of users (items). And we classify the user (items) which from target domain by exploiting the trained neural network and the users’ feature tags of target domain. Use the average rating values of the same type of users (items) to fill the missing value of target domain. We perform extensive experiments to show that our proposed model outperforms the state-of-the-art CF methods for the cross-domain recommendation task.