ID 原文 译文
22405 CTRR 算法通过 CTRR_LDA 模型求解推荐项目出现在特定环境上下文的概率，并结合上下文后过滤推荐算法，对用户进行推荐。 The CTRR algorithm uses the CTRR_LDA model and a post filtering strategy to recommend items to users in a specific environment context.
22406 CTRR_LDA 模型是在(LDA)模型的基础上，结合环境上下文和项目特征上下文，提出的项目与环境上下文的关联概率模型。 CTRR_LDA is an improved LDA model, which combines environment contexts and item feature contexts to calculate the probability of the item appeared.
22407 该模型将环境上下文划分为多个环境上下文因子，每个环境上下文因子表示为 K 维的主题分布，挖掘环境上下文因子中项目出现的潜在主题特征。 In this model, the environment context is divided into multiple environment context factors. Each environment context factor can be expressed as a K-dimensional topic distribution. Then the CTRR_LDA model is used to mine the latent topic of the items in each environment context factor.
22408 利用 LDOS-CoMoDa 网站上真实的电影数据集进行实验，验证了算法的可靠性。 According to the experimental results on the LDOS-CoMoDa datasets, the reliability of algorithm is validated in the context-aware recommendation scenario.
22409 该文结合掌纹图像的纹理特点，对原始韦伯局部描述子(WLD)中的差分激励和梯度方向进行改进，提出双 Gabor 方向韦伯局部描述子(DGWLD)，以提高掌纹识别率。 In order to improve the palmprint recognition rate, this paper improves differential excitation and gradient orientation of Weber Local Descriptor (WLD) based on the texture features of palmprint images, and proposes a Double Gabor orientation Weber Local Descriptor (DGWLD).
22410 在构建新的差分激励图时，通过加入邻域像素点与中心像素点间灰度差分的方向信息，扩大异类掌纹间的差异。 The directional information of the difference between the neighborhood pixels and the central pixel is considered to enlarge the difference between palmprint, when constructing the new differential excitation map.
22411 同时，采用双 Gabor 方向代替原始的梯度方向，减小平移和旋转对识别的影响。 At the same time, gradient orientation is replaced by double Gabor orientation to reduce the influence of translation and rotation.
22412 此外，为了更好地衡量特征间的相似度，使用交叉匹配算法，进一步提升识别率。 In addition, a feature cross matching algorithm is used for further improve the recognition rate.
22413 在Poly U, MSpalmprint 和 CASIA 掌纹库上进行实验，识别率均达到 100%。 Experiments on PolyU, MSpalmprint and CASIA palmprint databases show that the recognition rate is up to 100%.
22414 实验的结果表明，与其它局部描述子和已有改进的 WLD 方法相比，该文方法具有更高的识别率和更低的等错误率。 The experimental results show that the proposed method is superior in terms of identification rate and equal error rate compared with other local descriptor methods and improved WLD methods.