图 书 馆
基于离散排序的自学习矩阵因子分解Discrete Ranking based Matrix Factorization with Self Paced Learning |
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| 课程网址: | http://videolectures.net/kdd2018_zhang_ranking_factorization/ |
| 主讲教师: | Yan Zhang |
| 开课单位: | 中国科学技术大学 |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The efficiency of top-k recommendation is vital to large-scale recommender systems. Hashing is not only an efficient alternative but also complementary to distributed computing, and also a practical and effective option in a computing environment with limited resources. Hashing techniques improve the efficiency of online recommendation by representing users and items by binary codes. However, objective functions of existing methods are not consistent with ultimate goals of recommender systems, and are often optimized via discrete coordinate descent, easily getting stuck in a local optimum. To this end, we propose a Discrete Ranking-based Matrix Factorization (DRMF) algorithm based on each user’s pairwise preferences, and formulate it into binary quadratic programming problems to learn binary codes. Due to non-convexity and binary constraints, we further propose self-paced learning for improving the optimization, to include pairwise preferences gradually from easy to complex. We finally evaluate the proposed algorithm on three public real-world datasets, and show that the proposed algorithm outperforms the state-of-the-art hashing-based recommendation algorithms, and even achieves comparable performance to matrix factorization methods. |
| 关 键 词: | 基于离散排序; 自学习矩阵因子分解; 大规模推荐系统; 真实世界数据集; 哈希技术 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-03-15：cyh |
| 最后编审: | 2023-03-15：cyh |
| 阅读次数: | 28 |