适用于RPA工具的卷积序列推荐算法

在机器人流程自动化(Robotic Process Automation， RPA)软件中，经常采用序列推荐系统让机器人完成判断、选择等人工处理的任务。然而常用的序列推荐系统受限于序列信息的提取困难等问题，难以得到广泛应用。为了解决这一问题，构建一种基于Inception的卷积序列推荐模型，把时间和潜在空间中的用户行为序列信息嵌入进一幅“图像”中，并通过动态和静态2种不同的卷积层提取其中的局部特征，全面地提取用户的短期兴趣偏好，同时将用户嵌入矩阵作为用户的长期兴趣偏好嵌入到卷积层的输出中，共同构建完整的用户兴趣偏好，提升推荐性能。通过在3种公开数据集MovieLens 1M、Gowalla、Steam上分别进行实验，验证了基于Inception的卷积序列推荐模型的性能优于最新的序列推荐模型，在Top-N序列推荐的3种评价指标中（精确率、召回率、平均AP值），平均提升幅度在10%左右，单个指标上的最大提升幅度为14%。

Convolutional Sequence Recommendation Algorithm for RPA Softwares

In RPA (Robotic Process Automation) softwares, sequence recommendation systems are often used to complete manual processing tasks such as judgment and selection. However, the commonly used sequence recommendation system is limited by the difficulty of extracting sequence information, so it is difficult to be widely used. In order to solve this problem, this paper constructs a convolutional sequence recommendation model based on Inception. It embeds user behavior sequence information in time and latent space into an “image”, and extracts local features through dynamic and static convolutional layers. It can fully extract the user’s short-term interest preferences, and embed the user embedding matrix as the user’s long-term interest preferences into the output of the convolutional layer. They work together to build a complete set of user interest preferences and improve recommendation performance. Through experiments on three public data sets MovieLens 1M, Gowalla, and Steam, it is verified that the performance of the convolutional sequence recommendation model based on Inception is better than the latest sequence recommendation model. Among the three evaluation indicators of Top-N series (Precision@N, Recall@N, MAP), the average increase is about 10%, and the maximum increase on a single index is 14%.