一种基于耦合对象相似度的项目推荐算法

Coupled Object Similarity Based Item Recommendation Algorithm

推荐系统根据用户的偏好为用户推荐个性化的信息、产品和服务等,能够帮助用户有效解决信息过载问题。基于内容的协同过滤算法缺少合适的度量指标用来计算项目之间的相似度。提出一种基于耦合对象相似度的项目推荐算法,即通过耦合对象相似度捕获项目特征频率分布相似性和特征依赖聚合相似度。首先从项目文本中抽取项目的关键特征,然后利用耦合对象相似度构建项目相似度模型,最后使用协同过滤的方法为活动用户推荐用户可能感兴趣的项目。在真实数据集上的实验结果表明,基于耦合对象相似度的推荐算法可以有效解决基于内容推荐系统的项目相似度度量问题,在缺失大量项目特征数据的情况下改进传统基于内容推荐系统的推荐质量。

Recommender systems are very useful due to the huge volume of information available on the Web. It helps users alleviate the information overload problem by recommending users with the personalized information, products or services. For content-based recommendation algorithm, there are few suitable similarity measures for the content-based recommendation methods to compute the similarity between items. This paper proposed a coupled object similarity based item recommendation algorithm. Our method firstly extracts item features from items, and then constructs item similari- ty model by using coupled object similarity measure. The collaborative filtering technique is then used to produce the recommendations for active users. Experimental results show that our proposed recommendation algorithm effectively solves the problem of similarity measure between items for recommendation algorithm and improves the quality of traditional content-based recommendation when lacking most of the item features.