Abstract: 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%.

Key words: sequential recommendation, convolutional neural network, Inception network, robotic process automation(RPA), user preference