摘要
为了解决协同过滤推荐中的稀疏性问题,提出一种基于用户语义相似性的协同过滤推荐算法。算法考虑到项目之间内在的语义关系,通过构建领域本体来计算项目之间的语义相似度,并综合项目语义相似度和用户评分数据来度量用户语义相似性。实验结果表明,该算法在用户评分数据极端稀疏的情况下,依然可以获得较高的推荐质量。
In order to resolve the sparsity problem of collaborative filtering recommendation, a collaborative filte- ring recommendation algorithm based on user semantic similarity is presented. In consideration of the inner semantic relationship among items, the algorithm calculates the semantic similarity among items by constructing do main ontology, and combines the item semantic similarity and user rating data to measure the user semantic simi larity. The experiment result shows that this algorithm can effectively improve the recommendation quality in the condition of extreme sparsity of user rating data.
出处
《机械设计与制造工程》
2013年第1期70-72,共3页
Machine Design and Manufacturing Engineering
关键词
推荐系统
协同过滤
稀疏性
领域本体
语义相似性
Recommendation System
Collaborative Filtering
Sparsity
Domain Ontology
Semantic Similarity