融入专业度和用户相似性的跨域推荐算法  

Cross-domain recommendation algorithm integrating professionalism and user similarity

作　　者：赵厉宇哲 刘学军 徐新艳 ZHAO Li-yu-zhe;LIU Xue-jun;XU Xin-yan(Computer Science and Technology,Nanjing University of Technology,Nanjing 211816,China)

机构地区：[1]南京工业大学计算机科学与技术学院,江苏南京211816

出　　处：《计算机工程与设计》2019年第1期136-142,共7页Computer Engineering and Design

基　　金：江苏省重点研发计划(社会发展)基金项目(BE2015697);国家自然科学基金项目(61203072)

摘　　要：针对基于聚类型矩阵分解的跨域推荐算法过于泛化的问题,通过辅助领域中专业度高的相似用户在目标领域的评分来缓解跨域粗矩阵预测评分的泛化性,提出一种融入专业度和用户相似性的跨域推荐算法。在辅助领域计算冷启动用户与重叠用户的相似度,利用专业度高的相似用户在目标领域的评分预测其个性化评分,通过多域联合矩阵的分解聚类得到泛化的预测评分,两者进行加权综合,得到准确性较高的推荐项目。真实数据集上的实验结果表明,通过融入专业度和用户相似性,提出算法提高了冷启动用户的推荐准确性。Aiming at the problem of over-generalization of cross-domain recommendation algorithm based on clustering matrix decomposition,the generalization of the cross-domain rough matrix prediction score was improved by the similar users with high professionalism in the target field,so that a cross-domain recommendation algorithm was proposed that combined professionalism and user similarity.The overlap user’s similarity was calculated in the auxiliary field,high degree of similar users’ personalized score with high professionalism in the target area was used to predict for target users,and clustering decomposition of the crossdomain matrix was used to obtain generalized prediction score,the weighted personalized score was then got,improving the accuracy of the recommendation.Results of experiments on real data sets show that the proposed algorithm improves the recommendation accuracy for cold start users by integrating professionalism and user similarity.

关 键 词：专业度 跨域推荐 冷启动 矩阵分解 K-MEANS聚类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]