A Personalized System for Conversational Recommendations

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system

Towards a Taxonomy of Platforms for Conversational Agent Design

Software that interacts with its users through natural language, so-called conversational agents (CAs), is permeating our lives with improving capabilities driven by advances in machine learning and natural language processing. For organizations, CAs have the potential to innovate and automate a variety of tasks and processes, for example in customer service or marketing and sales, yet successful design remains a major challenge. Over the last few years, a variety of platforms that offer different approaches and functionality for designing CAs have emerged. In this paper, we analyze 51 CA platforms to develop a taxonomy and empirically identify archetypes of platforms by means of a cluster analysis. Based on our analysis, we propose an extended taxonomy with eleven dimensions and three archetypes that contribute to existing work on CA design and can guide practitioners in the design of CA for their organizations

Chatbots at Digital Workplaces – A Grounded-Theory Approach for Surveying Application Areas and Objectives

Background: Chatbots are currently on the rise as more and more researchers tackle this topic from different perspectives. Simultaneously, workplaces and ways of working are increasingly changing in the context of digitalization. However, despite the promised benefits, the changes still show problems that should be tackled more purposefully by chatbots. Application areas and underlying objectives of a chatbot application at digital workplaces especially have not been researched yet. Method: To solve the existing problems and close the research gap, we did a qualitative empirical study based on the grounded-theory process. Therefore, we interviewed 29 experts in a cross-section of different industry sectors and sizes. The experts work in the information systems domain or have profound knowledge of (future) workplace design, especially regarding chatbots. Results: We identified three fundamental usage scenarios of chatbots in seven possible application areas. As a result of this, we found both divisional and cross-divisional application areas at workplaces. Furthermore, we detected fifteen underlying objectives of a chatbot operation, which can be categorized from direct over mid-level to indirect ones. We show dependencies between them, as well. Conclusions: Our results prove the applicability of chatbots in workplace settings. The chatbot operation seems especially fruitful in the support or the self-service domain, where it provides information, carries out processes, or captures process-related data. Additionally, automation, workload reduction, and cost reduction are the fundamental objectives of chatbots in workplace scenarios. With this study, we contribute to the scientific knowledge base by providing knowledge from practice for future research approaches and closing the outlined research gap. Available at: https://aisel.aisnet.org/pajais/vol12/iss2/3

An investigation into a natural language interface for contact centers

Contact centres are the first point of contact between a company and a customer after the purchase of a product or service. These centres make use of contact centre agents to service customer queries. In the past contact centres hired as many agents as they could in order to service customers, which have led to an increase in personnel costs causing contact centres to become costly to run. Automation techniques were introduced to decrease personnel costs and one such technique is the Interactive Voice Response (IVR). The usability of IVR systems is, however, dismal. Customers would rather speak to a contact centre agent than navigate through the menu structure found in these systems. The menu structure has come under scrutiny because it is difficult to use and navigate, is often not aligned to caller usage patterns, and the menu options are long and vague. This research investigated whether a Natural Language Interface (NLI) could alleviate the problems inherent to IVR. NLIs, however, come with their own disadvantages of which the main ones are ambiguity and the loss of context of a conversation. Two prototypes were implemented, one of which resembled an IVR and the other an NLI (using ALICE concepts). An evaluation of two prototypes confirmed the advantages and disadvantages of these concepts in accordance to theory. A Hybrid prototype was proposed with the aid of two models. The model which proposed an NLI using a rule base was chosen for implementation. The Hybrid prototype was then evaluated against the NLI and IVR prototypes to deduce which prototype was the most effective, efficient and satisfying. The evaluation through the aid of descriptive and inferential statistics showed that the Hybrid prototype was the most usable prototype. The evaluation of the Hybrid prototype confirmed that a Hybrid approach could limit the shortcomings of IVR through the elimination of the menu structure found in these systems, thereby allowing users to state their queries in natural language. The incorporated rule base provided the Hybrid system with long term memory, eliminating one of the main disadvantages of NLIs

Natural interaction with a virtual guide in a virtual environment: A multimodal dialogue system

This paper describes the Virtual Guide, a multimodal dialogue system represented by an embodied conversational agent that can help users to find their way in a virtual environment, while adapting its affective linguistic style to that of the user. We discuss the modular architecture of the system, and describe the entire loop from multimodal input analysis to multimodal output generation. We also describe how the Virtual Guide detects the level of politeness of the user’s utterances in real-time during the dialogue and aligns its own language to that of the user, using different politeness strategies. Finally we report on our first user tests, and discuss some potential extensions to improve the system

Anthropomorphized chatbots in mental health applications

The number of people suffering from mental health disorders is steadily rising as a result of growing social and economic inequality, ongoing political conflict, and, not least, the COVID 19 pandemic. The rapid progress of artificial intelligence, and within it chatbots, presents an opportunity to address these deficiencies by reducing treatment barriers and providing economic benefits to service providers and consumers. To assure the effectiveness of chatbots in psychological health applications, they have to be accepted by users. A chatbot’s acceptance in mental health interventions is influenced by the benefits of intelligent machines, their expectation of nonjudgmental and unbiased support, and the effect of stigma on trust and belief in healthcare. Based on these insights, the experimental study examines whether users of psychological health apps more readily accept chatbots as opposed to physical health apps. Furthermore, the humanization of chatbots is a proven tool to enhance the quality of interaction with users. Thus, this dissertation additionally aims to investigate if a humanized chatbot entity affects their acceptance in the context of mental health apps. The results suggest that chatbots are more widely accepted in mental health applications compared to physical health applications. Moreover, the findings lead to the recommendation to implement humanized entities in chatbots within mental health applications. The results provide a rationale for conducting additional research to investigate the subject in greater depth. Due to the continuous development of AI, the utilization of chatbots in mental health care should be investigated continuously.O número de pessoas que sofrem de perturbações de saúde mental está a aumentar constantemente devido à desigualdade social e económica, conflitos políticos e da pandemia de COVID-19. O rápido progresso da inteligência artificial representa uma oportunidade para resolver estas perturbações, reduzindo os obstáculos ao tratamento e proporcionando benefícios económicos aos prestadores de serviços e aos pacientes. Para garantir a eficácia dos chatbots nas aplicações de saúde mental, estes têm de ser aceites pelos utilizadores. Esta aceitação nas intervenções de saúde mental é influenciada pelos benefícios das máquinas inteligentes, pela sua expectativa de apoio imparcial e sem juízos de valor e pelo efeito do estigma na confiança e na crença nos cuidados de saúde. Com base nestes conhecimentos, o estudo experimental examina se os chatbots são mais facilmente aceites pelos utilizadores de aplicações de saúde psicológica do que aplicações de saúde física. Além disso, a humanização dos chatbots é uma ferramenta comprovada para melhorar a qualidade da interacção com os utilizadores. Assim, esta dissertação tem como objetivo investigar se uma entidade chatbot humanizada afeta a sua aceitação no contexto de aplicações de saúde mental. Os resultados sugerem que os chatbots são melhor aceites em aplicações de saúde mental do que em aplicações de saúde física. Além disso, os resultados levam à recomendação da implementação de entidades humanizadas em chatbots dentro de aplicações de saúde mental. Devido ao desenvolvimento contínuo da IA, a utilização de chatbots nos cuidados de saúde mental deve ser investigada numa base contínua

Integration of AI into Customer Service: A Taxonomy to Inform Design Decisions

Artificial Intelligence (AI) is increasingly deployed in customer service for various service delivery tasks. Research and practice alike have extensively dealt with the use, benefits, and effects of AI solutions in customer service contexts. Nevertheless, knowledge on AI integration is dispersed and unsystematized. This paper addresses this gap by presenting a taxonomy to inform design decisions for the integration of AI into customer service with five meta-dimensions, 12 dimensions, and 32 characteristics. Through a rigorous and systematic development process comprising multiple iterations and evaluation episodes, state-of-the-art AI solutions from practice and the current state of knowledge from research were systematized to classify AI use cases. Thus, we contribute with systemized design knowledge to, both, the theoretical knowledge base as well as to practice for application. Eventually, we disclose future research avenues addressing certain meta-dimensions as well as the extension of the taxonomy itself

An Eye Gaze Model for Controlling the Display of Social Status in Believable Virtual Humans

Abstract—Designing highly believable characters remains a major concern within digital games. Matching a chosen personality and other dramatic qualities to displayed behavior is an important part of improving overall believability. Gaze is a critical component of social exchanges and serves to make characters engaging or aloof, as well as to establish character’s role in a conversation. In this paper, we investigate the communication of status related social signals by means of a virtual human’s eye gaze. We constructed a cross-domain verbal-conceptual computational model of gaze for virtual humans to facilitate the display of social status. We describe the validation of the model’s parameters, including the length of eye contact and gazes, movement velocity, equilibrium response, and head and body posture. In a first set of studies, conducted on Amazon Mechanical Turk using prerecorded video clips of animated characters, we found statistically significant differences in how the characters’ status was rated based on the variation in social status. In a second step based on these empirical findings, we designed an interactive system that incorporates dynamic eye tracking and spoken dialog, along with real-time control of a virtual character. We evaluated the model using a presential, interactive scenario of a simulated hiring interview. Corroborating our previous finding, the interactive study yielded significant differences in perception of status were found (p = .046). Thus, we believe status is an important aspect of dramatic believability, and accordingly, this paper presents our social eye gaze model for realistic procedurally animated characters and shows its efficacy. Index Terms—procedural animation, believable characters, virtual human, gaze, social interaction, nonverbal behaviour, video game