With the advent of the 5G digital era, cell phones are becoming ubiquitous in all aspects of our lives, and the increasing demand for remote interaction makes the app interaction experience an indispensable part of our lives. Due to the operational characteristics of gesture interaction in the interface of a smart terminal application (app), this mode of human-computer interaction has become the mainstream mode of human-computer interaction. Educational app is the result of a combination between mobile Internet technology and education, which not only provides a more efficient and convenient method of learning for each subject but also expands the possibilities for teaching each subject through intelligent interaction. On this basis, this paper proposes an educational app design method based on collaborative filtering recommendations and investigates ways to improve the use of mobile apps to create an interactive teaching mode. Simultaneously, this paper combines user activity, item popularity, and time factors to comprehensively measure user visibility of items and incorporates them into the collaborative filtering recommendation algorithm in order to effectively mitigate the effects of data sparsity and user selection bias and improve recommendation results.
Merchandise display design is a type of targeted design that involves the planning and implementation of merchandising, product display, visual communication, and the display of the retail environment in order to increase sales and suit the needs of both producers and customers. In this paper, we propose a mobile-based merchandise display system for the needs of merchandise display and virtual reality shopping in e-commerce. First, this paper analyzes the characteristics of several VR technologies and applies the WebVR 3D interaction engine to realize the online 3D virtual display of real-world and physical products to provide real-time interaction. Furthermore, this paper proposes a behavioral delay shared network model that fully combines historical browsing information to make accurate browsing recommendations to users. For computing the user’s interest representation, the model provides behavioral delay gated recurrent neural units. Finally, to provide a solution for mobile e-commerce apps, the behavioral delayed shared network model is implemented into the display system’s recommendation module.
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