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Chatbot

About: Chatbot is a research topic. Over the lifetime, 2415 publications have been published within this topic receiving 24372 citations. The topic is also known as: IM bot & AI chatbot.


Papers
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Journal ArticleDOI
TL;DR: A novel framework, that models intangible and tangible cultural objects into a unified data model, for supporting tourists journey outperforms the state-of-the art ones combining natural processing language, user profile information and historical activities is proposed.
Abstract: The technology improvement has radically changed how tourists perform their journey offering new services to enhance their cultural experience and to easily retrieve required information. In this paper we propose a novel framework, that models intangible and tangible cultural objects into a unified data model, for supporting tourists journey. In particular, a Micro-service architecture has been designed to provide several services whose tourists can access through a conversational agent based on the Seq2Seq model. Furthermore, we propose also an Enterprise Service Bus to ingest events automatically from promotional website or manually from public and/or private organization. We have evaluated the proposed framework according to the following two experiments: (i) the efficiency and efficacy of the Chatbot engine, showing that the use of GRU cells allows to obtain better results in terms of loss and accuracy with respect to LSTM one and (ii) the efficacy of the proposed framework according to the NASA-TLX asking to 50 users to interact with the Chatbot, demonstrating that the proposed approach outperforms the state-of-the art ones combining natural processing language, user profile information and historical activities.

29 citations

Journal ArticleDOI
22 Apr 2021
TL;DR: The authors developed a generalizable chatbot architecture (CASS) to provide social support for community members in an online health community, which can handle new inputs from users and generate a variety of responses to them.
Abstract: Chatbots systems, despite their popularity in today's HCI and CSCW research, fall short for one of the two reasons: 1) many of the systems use a rule-based dialog flow, thus they can only respond to a limited number of pre-defined inputs with pre-scripted responses; or 2) they are designed with a focus on single-user scenarios, thus it is unclear how these systems may affect other users or the community. In this paper, we develop a generalizable chatbot architecture (CASS) to provide social support for community members in an online health community. The CASS architecture is based on advanced neural network algorithms, thus it can handle new inputs from users and generate a variety of responses to them. CASS is also generalizable as it can be easily migrate to other online communities. With a follow-up field experiment, CASS is proven useful in supporting individual members who seek emotional support. Our work also contributes to fill the research gap on how a chatbot may influence the whole community's engagement.

29 citations

01 Jan 2019
TL;DR: In this paper, the authors describe possibilities, which are provided by open APIs, and how to use them for creating unified interfaces using the example of our bot based on Google API.
Abstract: The paper describes possibilities, which are provided by open APIs, and how to use them for creating unified interfaces using the example of our bot based on Google API. In last decade AI technologies became widespread and easy to implement and use. One of the most perspective technology in the AI field is speech recognition as part of natural language processing. New speech recognition technologies and methods will become a central part of future life because they save a lot of communication time, replacing common texting with voice/audio. In addition, this paper explores the advantages and disadvantages of well-known chatbots. The method of their improvement is built. The algorithms of Rabin-Karp and Knut-Pratt are used. The time complexity of proposed algorithm is compared with existed one.

29 citations

Journal ArticleDOI
TL;DR: An in vivo experimental evaluation of the design and implementation of a conversational music recommender system for the music domain finds that the best interaction mode is based on a mixed strategy that combines buttons and natural language.
Abstract: Conversational Recommender Systems (CoRSs) implement a paradigm that allows users to interact in natural language with the system for defining their preferences and discovering items that best fit their needs. CoRSs can be straightforwardly implemented as chatbots that, nowadays, are becoming more and more popular for several applications, such as customer care, health care, and medical diagnoses. Chatbots implement an interaction based on natural language, buttons, or both. The implementation of a chatbot is a challenging task since it requires knowledge about natural language processing and human–computer interaction. A CoRS might be particularly useful in the music domain since music is generally enjoyed in contexts when a standard interface cannot be exploited (driving, doing homeworks, running). However, there is no work in the literature that analytically compares different interaction modes for a conversational music recommender system. In this paper, we focus on the design and implementation of a CoRS for the music domain. Our CoRS consists of different components. The system implements content-based recommendation, critiquing and adaptive strategies, as well as explanation facilities. The main innovative contribution is that the user can interact through different interaction modes: natural language, buttons, and mixed. Due to the lack of available datasets for testing CoRSs, we carried out an in vivo experimental evaluation with the goal of investigating the impact of the different interaction modes on the recommendation accuracy and on the cost of interaction for the final user. The experiment involved 110 people, and 54 completed the whole process. The analysis of the results shows that the best interaction mode is based on a mixed strategy that combines buttons and natural language. In addition, the results allow to clearly understand which are the steps in the dialog that are particularly strenuous for the user.

29 citations

Proceedings Article
01 Jan 2018
TL;DR: The proposed research model hypothesizes the relationship between perceived autonomy, perceived competence, cognitive load, performance satisfaction, process satisfaction, and system satisfaction between chatbot system and website system and what factors determine satisfaction.
Abstract: Artificial intelligence (AI) technology is advancing, and its application are widely used in many fields. AI chatbots powered by natural language processing are being integrated in numerous industries. AI chatbots help to improve customer services and enhance customer experiences. There is lack of study about the interactions between human and chatbots. As chatbots are designed to make interactions that are closer and more personal to the user, the way users communicate and interact with chatbots will differ from nonAI machines. Based on self-determination theory, this research aims to study the differences in system satisfaction between chatbot system and website system, and what factors determine satisfaction. The proposed research model hypothesizes the relationship between perceived autonomy, perceived competence, cognitive load, performance satisfaction, process satisfaction, and system satisfaction. It is proposed that the model will be tested using experimental survey.

29 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023916
20221,413
2021564
2020617
2019528
2018326