Mobile Applications that Incorporate AI for Information Search and Recommendation: A Systematic Literature Review

The inclusion of artificial intelligence (AI) has become essential for mobile application development, allowing improved personalization and optimization of the user experience. Over the past decade, smart mobile devices have been observed to enhance user experience across a variety of needs. The main objective of this study is to evaluate AI-powered mobile applications that utilize intelligent search mechanisms and more accurate recommendations and analyze their impact on addressing these user needs. The databases used were Scopus, ScienceDirect, Web of Science, and EBSCO. A filtering process using Prisma and a document quality assessment process was performed to select the most relevant articles. The study posed four questions related to the topic. The results showed that mobile apps for search and recommendation are mainly based on hybrid approaches (collaborative and content-based filtering) and deep learning (autoencoders, LSTMs/transformers, and BERT-type semantic retrieval embeddings), complemented by classic techniques (matrix factorization, SVM, K-NN, and trees/boosting) and contextual personalization (location, time, activity). It was concluded that these AI additions benefited users and met their search and recommendation needs. Furthermore, these mechanisms are advancing by leaps and bounds, as we now talk about more precise search and recommendation with voice, images, and even video.