IEEE Access · 2019 · 12 citations · 18 references
Artificial IntelligenceEngineeringActive RecommendationInteractive SearchIntelligent SystemsCommunicationText MiningSpeech RecognitionNatural Language ProcessingIntelligent Voice SystemsInformation RetrievalAffective ComputingInteractive TopicSpeech PerceptionConversational Recommender SystemComputer ScienceSpeech CommunicationSpeech TechnologySpeech ProcessingHuman-computer InteractionSpeech InputArtsVoice TechnologySpeech InterfaceVoice InteractionInteractive Information Retrieval
Recently, the rapid development of mobile multimedia intelligent voice systems has affected the rapid changes in user interaction. Meanwhile, the continuous optimization of recommended algorithms makes the change of interaction mode from the original user's active search to the machine actively recommending continuously. It is an inevitable trend that multimedia intelligent voice dialogue system brings more efficient content services to users through active recommendation. However, the existing multimedia intelligent voice dialogue systems are mainly based on the user's expression content to analyze the user's subject willingness and then provide the reply contents to user. This kind of interaction only drives the topic through the user, which will cause some problems like single chat topic and missing points of interest. In order to address these problems, this paper proposes an interactive topic guiding model. This model uses the surrounding similar users and user context information to guide users' topics, and taking into account the harmony of human-computer interaction, the game method is adopted to select the optimal content response strategy by considering the user's and robot's emotional friendliness and content friendliness. The experiments show that the model proposed in this paper can effectively guide topics. Moreover, it has significant improvement in content richness compared with other models in human-machine natural interaction, which can provide some research ideas for guiding the topic in the mobile multimedia intelligent voice system.
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Sequence to Sequence Learning with Neural Networks
Ilya Sutskever, Oriol Vinyals, Quoc V. Le · arXiv (Cornell University) · 2014 · 13.3K citations · Full text
Topic Aware Neural Response Generation
Xing Chen, Wei Wu, Yu Wu et al. · Proceedings of the AAAI Conference on Artificial Intelligence · 2017 · 321 citations · Full text