Compressed knowledge transfer via factorization machine for heterogeneous collaborative recommendation
Compressed knowledge transfer via factorization machine for heterogeneous collaborative recommendation
复制标题
通过分解机压缩知识传输以实现异构协作推荐
DOI：
10.1016/j.knosys.2015.05.009
复制
发表时间：
2015-09
影响因子：
8.8
通讯作者：
Xu, Congfu
中科院分区：
文献类型：
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作者：
Ming, Zhong;Zhong, Hao;Wang, Xin;Xu, Congfu
近年来，协同推荐吸引了各种研究工作。然而，一个重要的问题设置，即，“一个用户检查了几个项目，但只评价了几个”，还没有得到太多的关注。本文从用户的内隐评价和外显评价的异质性反馈角度提出了异质性协同推荐问题。为了充分利用这种不同类型的反馈，我们提出了一种新的和通用的解决方案，称为压缩知识转移通过因式分解机（CKT-FM）。具体地，我们假设用户同质性和项目相关性的压缩知识，即，两类反馈背后的用户群和项目集是相似的，然后设计了一个两步迁移学习解决方案，包括压缩知识挖掘和集成。我们的解决方案能够通过降噪来传递高质量的知识，对个体级和集群级实体之间丰富的成对交互进行建模，并将潜在的不一致知识从隐式反馈调整为显式反馈。此外，时间复杂度和空间复杂度的分析表明，我们的解决方案比现有的异构反馈方法效率高得多。在两个大数据集上的大量实证研究表明，我们的解决方案明显优于最先进的非迁移学习方法w.r.t.。推荐准确性，并且比直接利用非隐式检查而不是压缩知识w.r.t. CPU时间和内存使用。因此，我们的CKT-FM在HCR知识转移的有效性和效率之间取得了良好的平衡。
Collaborative recommendation has attracted various research works in recent years. However, an important problem setting, i.e., “a user examined several items but only rated a few”, has not received much attention yet. We coin this problemheterogeneous collaborative recommendation(HCR) from the perspective of users’ heterogeneous feedbacks of implicit examinations and explicit ratings. In order to fully exploit such different types of feedbacks, we propose a novel and generic solution calledcompressed knowledge transfer via factorization machine(CKT-FM). Specifically, we assume that the compressed knowledge of user homophily and item correlation, i.e., user groups and item sets behind two types of feedbacks, are similar and then design a two-step transfer learning solution including compressed knowledge mining and integration. Our solution is able to transfer high quality knowledge via noise reduction, to model rich pairwise interactions among individual-level and cluster-level entities, and to adapt the potential inconsistent knowledge from implicit feedbacks to explicit feedbacks. Furthermore, the analysis on time complexity and space complexity shows that our solution is much more efficient than the state-of-the-art method for heterogeneous feedbacks. Extensive empirical studies on two large data sets show that our solution is significantly better than the state-of-the-art non-transfer learning method w.r.t. recommendation accuracy, and is much more efficient than that of leveraging therawimplicit examinations directly instead ofcompressedknowledge w.r.t. CPU time and memory usage. Hence, our CKT-FM strikes a good balance between effectiveness and efficiency of knowledge transfer in HCR.
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DOI：
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发表时间：
2012
期刊：
--
影响因子：
--
作者：
Steffen Rendle
通讯作者：
Steffen Rendle
DOI：
10.1145/1553374.1553454
发表时间：
2009-06
期刊：
--
影响因子：
--
作者：
Bin Li;Qiang Yang;X. Xue
通讯作者：
Bin Li;Qiang Yang;X. Xue
DOI：
--
发表时间：
2009-07
期刊：
--
影响因子：
--
作者：
Bin Li;Qiang Yang;X. Xue
通讯作者：
Bin Li;Qiang Yang;X. Xue
DOI：
--
发表时间：
2010
期刊：
--
影响因子：
--
作者：
Ryan P. Adams;George E. Dahl;Iain Murray
通讯作者：
Ryan P. Adams;George E. Dahl;Iain Murray
影响因子：
1.8
作者：
Arantxa Otegi;Iñaki San;X. Saralegi;Anselmo Peñas;Borja Lozano;Eneko Agirre
通讯作者：
Arantxa Otegi;Iñaki San;X. Saralegi;Anselmo Peñas;Borja Lozano;Eneko Agirre