计算机工程Issue(5)：6-13,8.DOI:10.3969/j.issn.1000-3428.2015.05.002
基于信任模型填充的协同过滤推荐模型
Collaborative Filtering Recommendation Model Based on Trust Model Filling
摘要
Abstract
Aiming at the problem of data sparsity in traditional collaborative filtering models,a collaborative filtering recommendation model based on trust model filling is proposed. The model gives emphasis to the trust attributes, and prefills the rating matrix by establishing trust model,in order to improve the data storage density. It obtains the similarity between items from the perspective of items and user attributes by similarity models. It coordinates the two types of similarity measurements by a self-adaptive coordination factor to gain final rating predictions of items. Experimental results,tested in different data sets,show that the newly proposed model can efficiently solve the problem of data sparsity in rating matrix,and provide better prediction accuracy of ratings involving an average improvement of 8%,compared with traditional collaborative filtering models.关键词
推荐系统/协同过滤/信任模型/用户属性/相似性模型/平均绝对误差Key words
recommendation system/collaborative filtering/trust model/user attribute/similarity model/Mean Absolute Error(MAE)分类
信息技术与安全科学引用本文复制引用
杨兴耀,于炯,吐尔根·依布拉音,廖彬,英昌甜..基于信任模型填充的协同过滤推荐模型[J].计算机工程,2015,(5):6-13,8.基金项目
国家自然科学基金资助项目(61262088,61063042) （61262088,61063042）
新疆大学优秀博士创新基金资助项目(XJUBSCX-2011007) （XJUBSCX-2011007）
新疆维吾尔自治区自然科学研究基金资助项目(2011211A011)。 （2011211A011）
