A personalized recommendation method based on collaborative ranking with random walk
A personalized recommendation method based on collaborative ranking with random walk
复制标题
一种基于随机游走协同排序的个性化推荐方法
DOI：
10.1007/s11042-022-11980-7
复制
发表时间：
2022-01
影响因子：
3.6
通讯作者：
Huaxiang Zhang
中科院分区：
文献类型：
--
作者：
Runqing Jiang;Shanshan Feng;Shoujia Zhang;Xi Li;Yan Yao;Huaxiang Zhang
为了切实提高推荐系统的性能，人们提出了多种混合推荐方法。最近，一些研究人员将排名的思想应用到产生可信结果的推荐系统中。协同排名是一种流行的基于排名的方法，它认为未评级的项目对用户的排名低于已评级的项目。遗憾的是，现有的协同排序方法只关注与用户和项目的部分关联，从而无法检测到一些可能提高推荐系统性能的特征。因此，这些方法继续受到数据稀疏性的困扰，并且不能很好地向个人用户推荐感兴趣的项目。为了解决这些问题，我们提出了一种基于协同排序和随机游走相结合的组合协作排序方法(ACR-RW)，该方法通过检测绝对和相对相关信息来根据用户的期望偏好对项目进行排序，从而将排名靠前的项目推荐给潜在感兴趣的用户。在ACR-RW的基础上，通过增加绝对关系信息和在集合中而不是在全局中定义偏序关系来改进协同排序方法，从而更好地描述和预测用户的偏好。最后，我们在三个真实世界的数据集上进行了实验，结果表明，我们的方法始终优于所有其他比较方法，证明了其在推荐任务中的有效性。
To improve the performance of recommender systems in a practical manner, many hybrid recommendation approaches have been proposed. Recently, some researchers apply the idea of ranking to recommender systems which yield plausible results. Collaborative ranking is a popular ranking based method, it regards that unrated items have lower rankings than rated items for a user. Unfortunately, the existing collaborative ranking approaches only focus on the partial associations with users and items, and thus fail to detect some features that could potentially improve the performance of the recommender systems. For this reason, these methods continue to suffer from data sparsity and do not work well for recommending an interesting item to an individual user. To address these issues, we present an Assembled Collaborative Ranking with Random Walk (ACR-RW) approach based on the combination of collaborative ranking and random walk method, which can be used to rank items according to expected user preferences by detecting both absolute and relative correlative information, in order to recommend top-ranked items to potentially interested users. On the basis of ACR-RW, we can improve the collaborative ranking approaches by adding absolute relationship information and defining the partial order relationship in assemblages rather than the global, so as to better describe and predict one user’s preference. Finally, we implement experiments on three real-world datasets, and the results show that our approach consistently outperforms all other comparative approaches, demonstrating its effectiveness for recommendation tasks.
登录
查看更多内容
DOI：
10.1109/dasc-picom-cbdcom-cyberscitech49142.2020.00066
发表时间：
2020-08
期刊：
2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
影响因子：
--
作者：
Chen Huang;Zhongyuan Gan;Feng Ye;Pan Wang;Moxuan Zhang
通讯作者：
Chen Huang;Zhongyuan Gan;Feng Ye;Pan Wang;Moxuan Zhang
DOI：
10.1145/3318464.3380562
发表时间：
2020-05
期刊：
Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
影响因子：
--
作者：
Yingxia Shao;Shiyu Huang;Xupeng Miao;B. Cui;Lei Chen
通讯作者：
Yingxia Shao;Shiyu Huang;Xupeng Miao;B. Cui;Lei Chen
DOI：
--
发表时间：
2012-12
期刊：
--
影响因子：
--
作者：
M. Volkovs;R. Zemel
通讯作者：
M. Volkovs;R. Zemel
DOI：
10.1109/bigdata.2017.8257991
发表时间：
2017-08
期刊：
2017 IEEE International Conference on Big Data (Big Data)
影响因子：
--
作者：
Haekyu Park;Jinhong Jung;U. Kang
通讯作者：
Haekyu Park;Jinhong Jung;U. Kang
DOI：
10.1145/2959100.2959182
发表时间：
2016-09
期刊：
Proceedings of the 10th ACM Conference on Recommender Systems
影响因子：
--
作者：
Dawen Liang;Jaan Altosaar;Laurent Charlin;D. Blei
通讯作者：
Dawen Liang;Jaan Altosaar;Laurent Charlin;D. Blei