基于移动用户上下文相似度的张量分解推荐算法

Tensor factorization recommendation algorithm based on context similarity of mobile user

针对移动服务推荐中用户上下文环境复杂多变和数据稀疏性问题,提出一种基于移动用户上下文相似度的张量分解推荐算法——UCS-TF。该算法组合用户间的多维上下文相似度和上下文相似可信度,建立用户上下文相似度模型,再对目标用户的K个邻居用户建立移动用户-上下文-移动服务三维张量分解模型,获得目标用户的移动服务预测值,生成移动推荐。实验结果显示,与余弦相似性方法、Pearson相关系数方法和Cosine1改进相似度模型相比,所提UCS-TF算法表现最优时的平均绝对误差(MAE)分别减少了11.1%、10.1%和3.2%;其P@N指标大幅提升,均优于上述方法。另外,对比Cosine1算法、CARS2算法和TF算法,UCS-TF算法在数据稀疏密度为5%、20%、50%、80%上的预测误差最小。实验结果表明UCS-TF算法具有更好的推荐效果,同时将用户上下文相似度与张量分解模型结合,能有效缓解评分稀疏性的影响。

To solve the problem of complex context and data sparsity, a new algorithm for the tensor decomposition based on context similarity of mobile user was proposed, namely UCS-TF （User-Context-Service Tensor Factorization recommendation）. Multi-dimensional context similarity model was established with combining the user context similarity and confidence of similarity. Then, K-neighbor information of the target user was applied to the three-dimensional tensor decomposition, composed by user, context and mobile-service. Therefore, the predicted value of the target user was obtained, and the mobile recommendation was generated. Compared with cosine similarity method, Pearson correlation coefficient method and the improved Cosine1 model, the Mean Absolute Error （MAE） of the proposed UCS-TF algorithm was reduced by 11.1%, 10.1% and 3.2% respectively; and the P@N index of it was also significantly improved, which is better than that of the above methods. In addition, compared with Cosine1 algorithm, CARS2 algorithm and TF algorithm, UCS-TF algorithm had the smallest prediction error on 5%, 20%, 50% and 80% of data density. The experimental results indicate that the proposed UCS-TF algorithm has better performance, and the user context similarity combining with the tensor decomposition model can effectively alleviate the impact of score sparsity.