| 摘要: |
| 协同过滤方法是当今大多数推荐系统的核心.传统的协同过滤方法专注于评分预测的准确性,然而实际推荐系统的推荐结果往往是项目的排序.针对这一问题,将排名学习领域的知识引入推荐算法,设计了一种基于评分矩阵局部低秩假设的成列协同排名算法.选择直接使用计算复杂度较低的成列损失函数来优化矩阵分解模型,并通过实验验证了其在运算速度上的显著提升.在3个实际推荐系统数据集上,与当下主流推荐算法的比较实验结果表明,该算法具有良好的性能. |
| 关键词: 推荐系统 协同过滤 排名学习 |
| DOI：10.13328/j.cnki.jos.004904 |
| 分类号: |
| 基金项目:北京市自然科学基金(4142042); 中央高校基本科研业务费专项资金(2015YJS049) |
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| Listwise Collaborative Ranking Based on the Assumption of Locally Low-Rank Rating Matrix |
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LIU Hai-Yang, WANG Zhi-Hai, HUANG Dan, SUN Yan-Ge
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School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
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| Abstract: |
| Collaborative filtering (CF) is the core of most of today's recommender systems. Conventional CF models focus on the accuracy of predicted ratings, while the actual output of recommender systems is a list of ranked items. In response to this problem, this research introduces technologies in the field of learning to rank into recommendation algorithms and proposes a listed collaborative ranking algorithm based on the assumption that the rating matrix is locally low-rank. It directly uses list-wise ranking loss function to optimize the matrix factorization model. Significant improvement on operation speed is achieved and verified by experiment. Experiments on three real-world recommender system datasets show that the proposed algorithm is a viable approach compared with existing recommendation algorithms. |
| Key words: recommender system collaborative filtering learn to rank |
