Abstract
In today’s society, there is an explosion of data, which brings with it the inherent challenge of dealing with this data. One such issue is analyzing data and making personalized suggestions of utterances to the users before any query is issued to the voice assistant. It should be possible to recommend the most relevant set of queries for quick access in advance to the user. These set of queries should be based on what the user might want to ask the voice assistant at a particular time based on the context such as the location, occasion and other features. Currently, ‘Bixby’ does not have a feature to recommend utterances to the users based on their demographics and usage patterns. Handling implicit data is problematic since it is difficult to analyze the user’s preferences. In this study, we analyze our strategy of recommending personalized utterances to users based on similar user profiles registered with ‘Bixby’ by taking into account several characteristics of the user such as the current context (time, place, occasion, etc.), demographics, utterances, and the frequency with which the utterances are recorded.
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Rajarajeswari, S. et al. (2023). Intelligent Bixby Recommender Systems. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 544. Springer, Cham. https://doi.org/10.1007/978-3-031-16075-2_25
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DOI: https://doi.org/10.1007/978-3-031-16075-2_25
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