关键词: 本体, 用户兴趣模型, 稳定性, 数据稀疏, 偏好方差

Abstract: To deal with low stability of user model and unsatisfied recommendation result existing in personalized service nowadays, ontology-based User Interest Model (UIM) was set up. Model stability was improved by update and model management was realized by establishing user group. To avoid sparse data in traditional collaborative filtering algorithm, personalized recommendation algorithm utilizing matrix clustering dimensionality-reduction decomposition was proposed. Nearest neighbors were calculated according to preferences variance, and the recommendation could be obtained subsequently. Then, the recommendation quality was improved. Finally, effectiveness of the model and algorithm was proved through a personalized movie recommendation system.

Key words: ontology, user interest model, stability, data sparseness, preferences variance