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The purpose of this paper is to alleviate the problem of poor robustness and over-fitting caused by large-scale data in collaborative filtering recommendation algorithms.

Interpreting user behavior from the probabilistic perspective of hidden variables is helpful to improve robustness and over-fitting problems. Constructing a recommendation network by variational inference can effectively solve the complex distribution calculation in the probabilistic recommendation model. Based on the aforementioned analysis, this paper uses variational auto-encoder to construct a generating network, which can restore user-rating data to solve the problem of poor robustness and over-fitting caused by large-scale data. Meanwhile, for the existing KL-vanishing problem in the variational inference deep learning model, this paper optimizes the model by the KL annealing and Free Bits methods.

The effect of the basic model is considerably improved after using the KL annealing or Free Bits method to solve KL vanishing. The proposed models evidently perform worse than competitors on small data sets, such as MovieLens 1 M. By contrast, they have better effects on large data sets such as MovieLens 10 M and MovieLens 20 M.

This paper presents the usage of the variational inference model for collaborative filtering recommendation and introduces the KL annealing and Free Bits methods to improve the basic model effect. Because the variational inference training denotes the probability distribution of the hidden vector, the problem of poor robustness and overfitting is alleviated. When the amount of data is relatively large in the actual application scenario, the probability distribution of the fitted actual data can better represent the user and the item. Therefore, using variational inference for collaborative filtering recommendation is of practical value.

This chapter reports on implementing a transformative business analytics course integrating AI and AI literacy at Gies College of Business, University of Illinois, Urbana-Champaign (UIUC). The course employs a novel teaching approach using Mathematica integrated with AI functionalities, including a GPT-powered chatbot. This integration facilitates an innovative ‘AI Mashup’ method, enhancing students’ ability to analyse diverse data types and produce compelling data narratives. Key course features include practical applications of computational recipes for complex analytics, interactive digital textbooks, and an emphasis on minimal coding for maximum functionality. Feedback from students indicates a high appreciation for the diverse applications enabled by powerful tools and the structured, beginner-friendly curriculum. The findings suggest that AI-integrated tools can enhance business analytics education by simplifying technical complexities and focusing on data storytelling, thereby preparing students more effectively for the digital economy’s demands with increased AI literacy.