一种基于本体语义相似度的协同过滤推荐方法  

Collaborative Filtering Recommendation Algorithm Based on Ontology Semantic Similarity

作　　者：吴正洋[1] 汤庸[1] 方家轩 董浩业 

机构地区：[1]华南师范大学计算机学院,广州510631

出　　处：《计算机科学》2015年第9期204-207,225,共5页Computer Science

基　　金：国家863计划重大项目(2013AA01A212);国家自然科学基金项目(61272067);广东省重大科技专项计划项目(2012A080104019)资助

摘　　要：协同过滤推荐是一种基于用户偏好的个性化推荐方法,一般包含两个步骤:首先根据用户或项目的标注信息计算出用户或项目的相似度,确定邻居集合;然后根据相似度进行排序推荐,其核心问题在于相似度的计算。为了更好地达到这一目的,近年来关于将用户社交网络信息融入相似度计算的方法受到广泛关注。用户的注册信息、项目评分和社交信息都可以作为用户比较的依据。基于此提出了通过构建用户本体,计算本体之间的语义相似度,从而找到相似用户集合,最终实现目标用户的推荐方法。该方法为本体技术与推荐系统的结合提供了一种思路,实验表明它能够在一定程度上提高推荐的准确度。Collaborative filtering recommendation is a personalized recommendation method based on users＇ preferences. It includes two steps. Firstly, according to the information marked by user or project, the similarity of the users or pro- jects is calculated and the neighbor set is determined. Secondly, sorting the similarity,user or project is recommended. During those process, similarity calculation is the core problem. In recent years, the method which uses users＇ social network information to calculate the similarity has gotten widely attention. Users~ registration information, the project score information,and social information can be used as a basis for comparing. Based on those,we built the ontology of users, calculated the semantic similarity between the ontologys, and then found a similar set of users. Through this method, we accomplished the purpose of personalized service. This method provides an idea to combine ontology tech- nology and recommendation system. Experiments show that this method can improve the accuracy of recommendation.

关 键 词：推荐系统 协同过滤 本体 语义相似度 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]