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| 一种融合项目特征和移动用户信任关系的推荐算法 |
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胡勋1,2, 孟祥武1,2, 张玉洁1,2, 史艳翠1,2
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1.智能通信软件与多媒体北京市重点实验室（北京邮电大学）, 北京 100876;2.北京邮电大学 计算机学院, 北京 100876
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| 摘要: |
| 协同过滤推荐系统中普遍存在评分数据稀疏问题.传统的协同过滤推荐系统中的余弦、Pearson 等方法都是基于共同评分项目来计算用户间的相似度；而在稀疏的评分数据中，用户间共同评分的项目所占比重较小，不能准确地找到偏好相似的用户，从而影响协同过滤推荐的准确度.为了改变基于共同评分项目的用户相似度计算，使用推土机距离（earth mover's distance，简称EMD）实现跨项目的移动用户相似度计算，提出了一种融合项目特征和移动用户信任关系的协同过滤推荐算法.实验结果表明：与余弦、Pearson 方法相比，融合项目特征的用户相似度计算方法能够缓解评分数据稀疏对协同过滤算法的影响.所提出的推荐算法能够提高移动推荐的准确度. |
| 关键词: 推土机距离 推荐系统 协同过滤 混合推荐 |
| DOI：10.13328/j.cnki.jos.004491 |
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| 基金项目:国家自然科学基金（60872051）；北京市教育委员会共建项目 |
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| Recommendation Algorithm Combing Item Features and Trust Relationship of Mobile Users |
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HU Xun1,2, MENG Xiang-Wu1,2, ZHANG Yu-Jie1,2, SHI Yan-Cui1,2
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1.Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia (Beijing University of Posts and Telecommunications), Beijing 100876, China;2.School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
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| Abstract: |
| The sparsity of user-item ratings is a common problem in collaborative filtering recommender systems. In traditional collaborative filtering recommender systems, similarity of users is often calculated with cosine and Pearson methods based on common ratings. When user-item ratings are sparse, the ratio of common rated items is less, and the accuracy of recommendations will be influenced because users with similar preferences can't be found accurately. To change calculation method of user similarity based on the same rated items, this paper applies EMD (earth mover's distance) to implement cross-item similarity calculation of mobile user and proposes a collaborative filtering recommendation method combining item features and trust relationship of mobile users. The experimental results show that, comparing with cosine and Pearson, user similarity calculation method combining item features can relieve influence of the sparsity of user-item ratings on collaborative filtering recommender systems. And the proposed recommender method can improve accuracy of mobile recommendations. |
| Key words: earth mover’s distance (EMD) recommender system collaborative filtering hybrid recommendation |
