一种改进相似性度量的协同过滤推荐算法

Improved Collaborative Filtering Recommendation Algorithm of Similarity Measure

协同过滤算法是目前电子商务推荐系统中最重要的技术之一,其中相似性度量方法的效果直接决定了推荐系统的准确率。传统的相似性度量方法主要关注用户共同评分项之间的相似度,却忽视了用户共同评分项和用户所有评分项之间的关系。用户共同评分项和用户所有评分项之间的关系可以通过Tanimoto系数来计算,然而Tanimoto系数是基于二值模式下的运算,因此直接运用于推荐系统中的效果并不理想。基于上述问题提出了修正的Tanimoto系数,并将用户共同评分项和用户所有评分项之间的关系融入到传统的相似性度量方法中。实验表明该算法在一定程度上提高了推荐的效率和准确度。

Collaborative filtering algorithm is one of the most important technologies in electronic commerce recommendation system.The accuracy of recommendation system directly depends on the effectiveness of the similarity measure.The methods of traditional similarity measure mainly focus on the similarity of user common rating items,but ignore the relationship between the user common rating items and all items the user rates.The relationship between the user common rating items and all items the user rates can be calculated by Tanimoto coefficient.However,Tanimoto coefficient is based on the mode of binary operation,which will not get the satisfactory result if it is directly applied in recommendation system.Aiming at the above problems,the improved Tanimoto coefficient was proposed,and the relationship between the user common rating items and all items the user rates was blended into the traditional similarity measure methods.Experiments show that,to a certain extent,the proposed collaborative filtering algorithm is more effective and accurate.