AI-Driven Personalization in Voice Search Systems

  • Authors

    • Dr. Ali Reza Professor, University of Tehran, Iran. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2018PI6W3H

    Published 04-04-2018

  • Artificial Intelligence, Voice Search, Personalization, Natural Language Processing, Machine Learning, Deep Learning, User Experience, Contextual Understanding, Speech Recognition, Sentiment Analysis

    Issue

    Vol. 1 No. 1 (2018)

    Section

    Articles

  • Abstract

    The integration of Artificial Intelligence (AI) into voice search systems has revolutionized user experiences by enabling personalized, context-aware interactions. This paper explores the role of AI-driven personalization in voice search, examining how AI technologies such as Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL) enhance the relevance and accuracy of voice-activated responses. We analyze the impact of personalized voice search on user engagement, decision-making, and brand interaction, highlighting both the opportunities and challenges it presents. Through a comprehensive review of current research and practical applications, this study provides insights into the future trajectory of AI in voice search personalization.​

  • References

    [1] Bolaños, D., Zweig, G., & Nguyen, P. (2009). Multi-scale personalization for voice search applications. In Proceedings of the Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT) (pp. 101–104). Association for Computational Linguistics. https://doi.org/10.3115/1620754.1620780

    [2] Zweig, G., & Chang, S. (2011). Personalizing Model M for voice search. In Proceedings of INTERSPEECH 2011 (pp. 2825–2828). International Speech Communication Association. https://doi.org/10.21437/Interspeech.2011-243

    [3] Hannák, A., Sapieżyński, P., Molavi Kakhki, A., Lazer, D., Mislove, A., & Wilson, C. (2017). Measuring personalization of web search. In Proceedings of the 26th International Conference on World Wide Web (WWW 2017) (pp. 527–536). https://doi.org/10.1145/3038912.3052652

    [4] Rao, J., Ture, F., He, H., Jojic, O., & Lin, J. (2017). Talking to your TV: Context-aware voice search with hierarchical recurrent neural networks. arXiv preprint arXiv:1705.04892. https://arxiv.org/abs/1705.04892

    [5] Wei, C.-K., Chung, C.-T., Lee, H.-Y., & Lee, L.-S. (2017). Personalized acoustic modeling by weakly supervised multi-task deep learning using acoustic tokens discovered from unlabeled data. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017) (pp. 5165–5169). https://doi.org/10.1109/ICASSP.2017.7953141

    [6] Kottur, S., Wang, X., & Carvalho, V. (2017). Exploring personalized neural conversational models. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI 2017) (pp. 3728–3734). https://doi.org/10.24963/ijcai.2017/521

    [7] Graus, M. P., Ferwerda, B., Schedl, M., Tkalčič, M., Willemsen, M. C., & Germanakos, P. (Eds.). (2017). Proceedings of the ACM Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces (HUMANIZE@IUI 2017). ACM. https://doi.org/10.1145/3039677

    [8] Dubiel, M. (2018). Towards human-like conversational search systems. In Proceedings of the 2018 Conference on Human Information Interaction & Retrieval (CHIIR '18) (pp. 348–350). ACM. https://doi.org/10.1145/3176349.3176360

    [9] Jeong, H., & Liu, Y. (2017). Development of a computational cognitive model for in-vehicle speech interfaces. In Proceedings of the 67th Annual Conference and Expo of the Institute of Industrial Engineers (pp. 2171–2176).

    [10] Trippas, J., Spina, D., Cavedon, L., & Sanderson, M. (2017). Crowdsourcing user preferences and query judgments for speech-only search. In Proceedings of the SIGIR 2017 Workshop on Conversational Approaches to Information Retrieval (CAIR 2017).

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