A Semi-supervised Learning Method for Hybrid Filtering

Abstract

Recommender systems are the auto systems of providing appropriate information and removing unappropriate information for users. The recommender systems are built based on two main information filtering techniques: Collaborative filtering and content-based filtering. Content-based filtering performs effectively on documents representing as text but has problems to select information features on multimedia data. Collaborative filtering perform well on all types of information but had problems when sparse data, new uses and new items. In this paper, we propose a new unify model between collaborative filtering and content-based filtering by a semi-supervised learning method. The model is built based on two semi-supervised procedures: the first procedure semi-supervise ratings set between users and item’s features, the second procedure semi-supervise ratings set between items and users features. The first procedure allows us to detect new items that is high suitable capability with the users. The second procedure allows us to detect new users that is high suitable ability with the items. Two procedures performed simultaneously and complement each other for suitable predicted values to improve recommender results. The experimental results on real data sets show that the proposed methods utilize effectively the advantages and limit significant disadvantages of baseline filtering methods.