With the rapid development of artificial intelligence (AI), many companies are moving towards automating their services using automated conversational agents. Dialogue-based conversational recommender agents, in particular, have gained much attention recently. The successful development of such systems in the case of natural language input is conditioned by the ability to understand the users’ utterances. Predicting the users’ intents allows the system to adjust its dialogue strategy and gradually upgrade its preference profile. Nevertheless, little work has investigated this problem so far. This paper proposes an LSTM-based Neural Network model and compares its performance to seven baseline Machine Learning (ML) classifiers. Experiments on a new publicly available dataset revealed The superiority of the LSTM model with 95% Accuracy and 94% F1-score on the full dataset despite the relatively small dataset size (9300 messages and 17 intents) and label imbalance.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    An LSTM-based Intent Detector for Conversational Recommender Systems


    Beteiligte:
    Jbene, Mourad (Autor:in) / Tigani, Smail (Autor:in) / Saadane, Rachid (Autor:in) / Chehri, Abdellah (Autor:in)


    Erscheinungsdatum :

    01.06.2022


    Format / Umfang :

    804489 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Ontology-based Conversational Recommender System for Electric Vehicles

    Alqadri, Muhammad Rizki / Baizal, Z. K. A. | IEEE | 2023


    LSTM-Based UAV Swarm Trajectory Prediction and Intent Recognition

    Ma, Dianqiu / Fu, Xianyi / Huang, Xueqin et al. | IEEE | 2025


    A Review of Dialogue Intent Identification Methods for Closed Domain Conversational Agents

    Papalkar, Sahil / Nagmal, Arati / Karve, Shreya et al. | IEEE | 2018


    TLSTM: A Transformer-LSTM Method for UAV Combat Intent Recognition

    Song, Yafei / Wang, Ke / Li, Lemin et al. | Springer Verlag | 2025


    Intent prediction of vulnerable road users from motion trajectories using stacked LSTM network

    Saleh, Khaled / Hossny, Mohammed / Nahavandi, Saeid | IEEE | 2017