App Personalization with Images Generated Using Artificial Intelligence

Personalized content such as app backgrounds, profile pictures, etc. help in creating a rich and interactive user experience for an app, potentially driving an increase in the active usage. Recent advancements in generative imagery can be used to generate images that are personalized to the user. However, current image generation techniques require a user to explicitly provide prompts to the generation model. This disclosure describes techniques that use generative artificial intelligence to automatically generate customized images for use as an app background or in other contexts. Prompts for image creation are generated based on user-permitted contextual information by using a large language model or other technique. The generated images are filtered by an image recommendation model and provided for user selection to customize the app experience

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502