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 ma...
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2025
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| author | Aliaga, Mijael R. Llacctahuaman, Jhosep S. Esquivel, Carla N. Saboya Rios, Nemias |
| author_browse | Aliaga, Mijael R. Esquivel, Carla N. Llacctahuaman, Jhosep S. Saboya Rios, Nemias |
| author_facet | Aliaga, Mijael R. Llacctahuaman, Jhosep S. Esquivel, Carla N. Saboya Rios, Nemias |
| author_sort | Aliaga, Mijael R. |
| collection | Producción científica |
| description | 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. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/1108 |
| institution | UPeU |
| language | English |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/11082026-10-30T16:15:13ZMobile Applications that Incorporate AI for Information Search and Recommendation: A Systematic Literature ReviewAliaga, Mijael R.Llacctahuaman, Jhosep S.Esquivel, Carla N.Saboya Rios, NemiasAI algorithmsrecommendation algorithmsmobile applicationssearch algorithmsAI for informationThe 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.Articlehttp://thesai.org/Downloads/Volume16No8/Paper_63-Mobile_Applications_that_Incorporate_AI_for_Information_Search.pdfhttps://doi.org/10.14569/ijacsa.2025.016086310.14569/ijacsa.2025.0160863https://hdl.handle.net/20.500.15037/1108W4413953585enInternational Journal of Advanced Computer Science and Applications2156-55702158-107X2025-01-012026-06-13T09:46:32Z2026-06-13T09:46:32Z |
| spellingShingle | AI algorithms recommendation algorithms mobile applications search algorithms AI for information Aliaga, Mijael R. Llacctahuaman, Jhosep S. Esquivel, Carla N. Saboya Rios, Nemias Mobile Applications that Incorporate AI for Information Search and Recommendation: A Systematic Literature Review |
| title | Mobile Applications that Incorporate AI for Information Search and Recommendation: A Systematic Literature Review |
| title_full | Mobile Applications that Incorporate AI for Information Search and Recommendation: A Systematic Literature Review |
| title_fullStr | Mobile Applications that Incorporate AI for Information Search and Recommendation: A Systematic Literature Review |
| title_full_unstemmed | Mobile Applications that Incorporate AI for Information Search and Recommendation: A Systematic Literature Review |
| title_short | Mobile Applications that Incorporate AI for Information Search and Recommendation: A Systematic Literature Review |
| title_sort | mobile applications that incorporate ai for information search and recommendation a systematic literature review |
| topic | AI algorithms recommendation algorithms mobile applications search algorithms AI for information |
| topic_facet | AI algorithms recommendation algorithms mobile applications search algorithms AI for information |
| url | http://thesai.org/Downloads/Volume16No8/Paper_63-Mobile_Applications_that_Incorporate_AI_for_Information_Search.pdf https://doi.org/10.14569/ijacsa.2025.0160863 https://hdl.handle.net/20.500.15037/1108 |
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