A Novel Recommendation Model Regularized with User Trust and Item Ratings
A Novel Recommendation Model Regularized with User Trust and Item Ratings
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DOI：
10.1109/tkde.2016.2528249
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发表时间：
2016-07-01
影响因子：
8.9
通讯作者：
Yorke-Smith, Neil
中科院分区：
文献类型：
--
作者：
Guo, Guibing;Zhang, Jie;Yorke-Smith, Neil
我们提出了信任SVD，一个基于信任的矩阵分解技术的建议。TrustSVD将多个信息源集成到推荐模型中，以减少数据稀疏和冷启动问题及其对推荐性能的影响。通过对四个真实数据集的社会信任数据的分析表明，在推荐模型中不仅要考虑评分和信任的显性影响，还要考虑隐性影响。因此，TrustSVD建立在最先进的推荐算法SVD++（其使用评级项目的显式和隐式影响）之上，通过进一步合并可信和信任用户对活跃用户的项目预测的显式和隐式影响。该技术是第一个扩展SVD++与社会信任信息。在4个数据集上的实验结果表明，与其他10种推荐方法相比，TrustSVD具有更好的推荐准确率。
We propose Trust SVD, a trust-based matrix factorization technique for recommendations. TrustSVD integrates multiple information sources into the recommendation model in order to reduce the data sparsity and cold start problems and their degradation of recommendation performance. An analysis of social trust data from four real-world data sets suggests that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. TrustSVD therefore builds on top of a state-of-the-art recommendation algorithm, SVD++ (which uses the explicit and implicit influence of rated items), by further incorporating both the explicit and implicit influence of trusted and trusting users on the prediction of items for an active user. The proposed technique is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that TrustSVD achieves better accuracy than other ten counterparts recommendation techniques.