基于语义相似性的资源协同过滤技术研究

A Study on Item-Based Collaborative Filtering Algorithm Using Semantic Similarity

为解决协同过滤推荐系统中所存在的可扩展性、稀疏性等问题带来的推荐性能下降,提出新的基于资源语义知识协同过滤算法,算法综合考虑了资源语义和用户评价的影响,改善基于资源协同过滤算法性能.实验表明,基于资源语义的协同过滤算法相对于传统协同过滤算法可提高推荐性能.

In a recommendation system based on collaborative filtering(CF), in order to resolve efficiently some problems such as the scalability and sparsity problems the quality of recommendation system will tend to be decreased dramatically. A new CF algorithm based on semantic knowledge of items is presented. The algorithm takes synthetically into account the influence of item semantic and user rating to enhance the item-based CF. Experimental results indicate that the algorithm can achieve better prediction accuracy and provide better recommendation results than with the traditional CF algorithms.