Smart home technologies, like cameras, door locks, and speakers, are increasingly used in our everyday lives. However, their continuous data collection and internet connectivity pose various security risks. While research on smart home security has mainly focused on technological aspects, human experience and societal factors also play a crucial role. Various human and social factors, such as user experience with smart home devices, security design processes, and government regulations, are intertwined and influence each other, affecting smart home security. It is therefore important to understand and consider these interconnected factors in technology design to secure homes that contain increasingly connected devices. This scoping review provides an overview of current human-centered studies (N=102) on smart home security, which aims to help researchers and practitioners better navigate this field. We present a conceptual framework that outlines key challenges in ensuring smart home security with a synthesis of insights on contributing human factors. We then summarize general security design principles and map existing user-centred security approaches in smart homes, and highlight research directions for future investigation. Beyond mapping existing studies, the review reveals a growing emphasis on engaging multiple stakeholders, especially smart home users, in shaping human-centered security.

Large language models (LLMs) have enabled new tools in requirements engineering (RE), often in the form of intelligent agents or virtual assistants. These tools can transform how software engineers perform RE tasks and interact with stakeholders. However, existing research primarily focuses on showcasing the capabilities of these tools rather than their design and evaluation in RE-specific contexts. This limits our understanding of their practical value and hinders broader adoption. To address this gap, we propose a reference model to guide the design, use, and evaluation of intelligent RE agents. Our work introduces new RE use cases, along with evaluation metrics for intelligent RE agents. We present a study design to support systematic development and share early findings demonstrating the feasibility of our approach. The use cases show how agents can add value for RE practitioners, while our synthesized catalogue supports tool evaluation. Finally, our analysis of commercial agents reveals that these tools already support certain aspects of the envisioned RE use cases.

Machine learning-augmented applications have the potential to be powerful tools for decision-making in healthcare. However, healthcare is a complex domain that presents many challenges. These challenges, such as medical errors, clinician–patient relationships and treatment preferences, must be addressed to ensure fairness in ML-augmented healthcare applications. To better understand the influence these challenges have on fairness, 16 experienced engineers and designers with domain knowledge in healthcare technology were interviewed about how they would prioritise fairness in 3 healthcare scenarios (well-being improvement, chronic illness management, acute illness treatment). Using a template analysis, this work identifies the key considerations in the creation of fair ML for healthcare. These considerations clustered into categories related to technology, healthcare context and user perspectives. To explore these categories, we propose the stakeholder fairness conceptual model. This framework aids designers and developers in understanding the complex considerations that stem from the building, management and evaluation of ML-augmented healthcare applications, and how they affect the expectations of fairness. This work then discusses how this model may be applied when the health technology is directly provisioned to users, without a healthcare provider managing its use or adoption. This article contributes to the understanding of fairness requirements in healthcare, including the effect of healthcare errors, clinician-application collaboration and how the evaluation of healthcare technology becomes part of the fairness design process.

As AI systems become increasingly integrated into our lives, the need to support appropriate human understanding of AI continues to grow. With new AI capabilities being deployed in different contexts, human-centered explainability is crucial to ensure people can interact with novel AI systems safely and effectively. To address evolving explainability needs, the field of Explainable AI (XAI) has produced numerous frameworks. But what do these frameworks entail and how can they be used in practice? What drives their development? As AI systems continue to grow in complexity, it is important to understand and reflect upon the value of these frameworks and their potential to address upcoming human-centered needs for XAI. Towards this, we performed a scoping review following the PRISMA-ScR procedure, gathering and analyzing a corpus of 73 papers to understand how XAI frameworks can support different stages of human-centered XAI design. We present a unified model and a set of guiding questions to help identify, compare and select relevant XAI frameworks across various design stages, making it easier for designers and researchers to apply human-centered approaches in real-world XAI contexts. We also analyze how frameworks are developed and evaluated, highlighting gaps and opportunities to improve both methodological as well as existing HCXAI practices.

Security attacks are rising, as evidenced by the number of reported vulnerabilities. Among them, unknown attacks, including new variants of existing attacks, technical blind spots or previously undiscovered attacks, challenge enduring security. This is due to the limited number of techniques that diagnose these attacks and enable the selection of adequate security controls. In this paper, we propose an automated technique that detects and diagnoses unknown attacks by identifying the class of attack and the violated security requirements, enabling the selection of adequate security controls. Our technique combines anomaly detection to detect unknown attacks with abductive reasoning to diagnose them. We first model the behaviour of the smart home and its requirements as a logic program in Answer Set Programming (ASP). We then apply Z-Score thresholding to the anomaly scores of an Isolation Forest trained using unlabeled data to simulate unknown attack scenarios. Finally, we encode the network anomaly in the logic program and perform abduction by refutation to identify the class of attack and the security requirements that this anomaly may violate. We demonstrate our technique using a smart home scenario, where we detect and diagnose anomalies in network traffic.We evaluate the precision, recall and F1-score of the anomaly detector and the diagnosis technique against 18 attacks from the ground truth labels provided by two datasets, CICIoT2023 and IoT-23. Our experiments show that the anomaly detector effectively identifies anomalies when the network traces are strong indicators of an attack. When provided with sufficient contextual data, the diagnosis logic effectively identifies true anomalies, and reduces the number of false positives reported by anomaly detectors. Finally, we discuss how our technique can support the selection of adequate security controls.

Music-based reminiscence has the potential to positively impact the psychological well-being of older adults. However, the aging process and physiological changes, such as memory decline and limited verbal communication, may impede the ability of older adults to recall their memories and life experiences. Given the advanced capabilities of generative artificial intelligence (AI) systems, such as generated conversations and images, and their potential to facilitate the reminiscing process, this study aims to explore the design of generative AI to support music-based reminiscence in older adults. This study follows a user-centered design approach incorporating various stages, including detailed interviews with two social workers and two design workshops (involving ten older adults). Our work contributes to an in-depth understanding of older adults’ attitudes toward utilizing generative AI for supporting music-based reminiscence and identifies concrete design considerations for the future design of generative AI to enhance the reminiscence experience of older adults.

An increasing number of recommendation systems try to enhance the overall user experience by incorporating conversational interaction. However, evaluating conversational recommender systems (CRSs) from the user’s perspective remains elusive. The GUI-based system evaluation criteria may be inadequate for their conversational counterparts. This article presents our proposed unifying framework, CRS-Que , to evaluate the user experience of CRSs. This new evaluation framework is developed based on ResQue , a popular user-centric evaluation framework for recommender systems. Additionally, it includes user experience metrics of conversation (e.g., understanding, response quality, humanness) under two dimensions of ResQue (i.e., Perceived Qualities and User Beliefs). Following the psychometric modeling method, we validate our framework by evaluating two conversational recommender systems in different scenarios: music exploration and mobile phone purchase . The results of the two studies support the validity and reliability of the constructs in our framework and reveal how conversation constructs and recommendation constructs interact and influence the overall user experience of the CRS. We believe this framework could help researchers conduct standardized user-centric research for conversational recommender systems and provide practitioners with insights into designing and evaluating a CRS from users’ perspectives.

Music can affect the human brain and cognition. Melodies and lyrics that resonate with us can awaken our inner feelings and thoughts; being in touch with these feelings and expressing them allow us to understand ourselves better and increase our self-awareness. To support self-awareness elicited by music, we designed a novel conversational agent (CA) that guides users to become self-aware and express their thoughts when they listen to music. Moreover, we investigated two prominent design factors in the CA, proactive guidance and social information. We then conducted a 2x2 between-subjects experiment (N = 90) to investigate how the two design factors affect self-awareness, user acceptance, and mental well-being. The results of a five-day user study reveal that high proactive guidance and social information increased self-awareness, but high proactive guidance tended to influence perceived autonomy and usefulness negatively. Further, users’ subjective feedback revealed the CA’s potential to support mental well-being.

Online music platforms that include social networking features sometimes become supportive social communities where young people can disclose their emotional distress and receive support. However, few studies have examined young people's disclosure in social music communities or the support they provide or receive. In this study, which focuses on a large online music platform as a research site, we used mixed methods to analyze young users' comments (N = 163) and the associated replies (N = 2,732) related to their psychological distress (e.g., depression, anxiety, stress, and loneliness). We found that the main types of comments involved experience sharing, and these comments often invoked peer support in the form of encouragement, caring, or self-disclosure. We also conducted an interview study with 13 young users of our research site to understand their perceptions of and motives for engaging in disclosure and support. The interviewees stated that music-induced and comment-induced emotional resonance was the main reason for their disclosure and support. Finally, we discussed the implications of our findings for designing a supportive social music community to benefit youth mental health.

Conversational recommender systems (CRSs) imitate human advisors to assist users in finding items through conversations and have recently gained increasing attention in domains such as media and e-commerce. Like in human communication, building trust in human-agent communication is essential given its significant influence on user behavior. However, inspiring user trust in CRSs with a “one-size-fits-all” design is difficult, as individual users may have their own expectations for conversational interactions (e.g., who, user or system, takes the initiative), which are potentially related to their personal characteristics. In this study, we investigated the impacts of three personal characteristics, namely personality traits, trust propensity, and domain knowledge, on user trust in two types of text-based CRSs, i.e., user-initiative and mixed-initiative. Our between-subjects user study (N=148) revealed that users’ trust propensity and domain knowledge positively influenced their trust in CRSs, and that users with high conscientiousness tended to trust the mixed-initiative system.

Dialogue-based conversational recommender systems (DCRSs) have become a new trend in recommender systems (RSs), allowing users to communicate with the system in natural language to facilitate feedback provision and product exploration. However, little work has been done to empirically study user perception of and interaction with such systems and, more importantly, how to best support users in providing feedback on the recommendation they receive. In this article, we aim to develop effectivecritiquingmechanisms for DCRS to improve its feedback elicitation process (i.e., allowing users tocritiquethe current recommendation during the dialogue). Specifically, we have implemented three prototype systems featuring three different critiquing techniques, respectively, i.e.,user-initiated critiquing, progressive system-suggested critiquing, andcascading system-suggested critiquing. We have then conducted two task-oriented user studies involving 292 subjects to evaluate the three prototypes. In particular, we consider two typical types of user tasks in RSs: basic recommendation task (BRT, i.e., looking for items according to the user’s preferences), and exploration-oriented task (EOT, i.e., exploring different types of items). Results show that EOT stimulates more user interaction, while BRT results in higher user satisfaction. Moreover, when users perform EOT, the type of critiquing techniques is more likely to influence user perception and moderate the relationships between certain interaction metrics and users’ perceived serendipity. The findings suggest effective critiquing techniques to enhance the interaction between users and the recommendation chatbot when the system makes recommendations for different purposes.

An increasing number of recommender systems enable conversational interaction to enhance the system’s overall user experience (UX). However, it is unclear what qualities of a conversational recommender system (CRS) are essential to determine the success of a CRS. This paper presents a model to capture the key qualities of conversational recommender systems and their related user experience aspects. Our model incorporates the characteristics of conversations (such as adaptability, understanding, response quality, rapport, humanness, etc.) in four major user experience dimensions of the recommender system: User Perceived Qualities, User Belief, User Attitudes, and Behavioral Intentions. Following the psychometric modeling method, we validate the combined metrics using the data collected from an online user study of a conversational music recommender system. The user study results 1) support the consistency, validity, and reliability of the model that identifies seven key qualities of a CRS; and 2) reveal how conversation constructs interact with recommendation constructs to influence the overall user experience of a CRS. We believe that the key qualities identified in the model help practitioners design and evaluate conversational recommender systems.

Dialogue-based conversational recommender systems allow users to give language-based feedback on the recommended item, which has great potential for supporting users to explore the space of recommendations through conversation. In this work, we consider incorporating critiquing techniques into conversational systems to facilitate users’ exploration of music recommendations. Thus, we have developed a music chatbot with three system variants, which are respectively featured with three different critiquing techniques, i.e., user-initiated critiquing (UC), progressive system-suggested critiquing (Progressive SC), and cascading system-suggested critiquing (Cascading SC). We conducted a between-subject study (N=107) to compare these three types of systems with regards to music exploration in terms of user perception and user interaction. Results show that both UC and SC are useful for music exploration, while users perceive higher diversity of recommendations with the system that offers Cascading SC and perceive more serendipitous with the system that offers Progressive SC. In addition, we find that the critiquing techniques significantly moderate the relationships between some interaction metrics (e.g., number of listened songs, number of dialogue turns) and users’ perceived helpfulness and serendipity during music exploration.

To develop a multi-turn dialogue-based conversational recommender system (DCRS), it is important to predict users' intents behind their utterances and their satisfaction with the recommendation, so as to allow the system to incrementally refine user preference model and adjust its dialogue strategy. However, little work has investigated these issues so far. In this paper, we first contribute with two hierarchical taxonomies for classifying user intents and recommender actions respectively based on grounded theory. We then define various categories of feature considering content, discourse, sentiment, and context to predict users' intents and satisfaction by comparing different machine learning methods. The experimental results for user intent prediction task show that some models (such as XGBoost and SVM) can perform well in predicting user intents, and incorporating context features into the prediction model can significantly boost the performance. Our empirical study also demonstrates that leveraging dialogue behavior features (i.e., including both user intents and recommender actions) can achieve good results in predicting user satisfaction.

Critiquing-based recommender systems aim to elicit more accurate user preferences from users' feedback toward recommendations. However, systems using a graphical user interface (GUI) limit the way that users can critique the recommendation. With the rise of chatbots in many application domains, they have been regarded as an ideal platform to build critiquing-based recommender systems. Therefore, we present MusicBot, a chatbot for music recommendations, featured with two typical critiquing techniques, user-initiated critiquing (UC) and system-suggested critiquing (SC). By conducting a within-subjects (N=45) study with two typical scenarios of music listening, we compared a system of only having UC with a hybrid critiquing system that combines SC with UC. Furthermore, we analyzed the effects of four personal characteristics,musical sophistication (MS), desire for control (DFC), chatbot experience (CE), and tech savviness (TS), on the user's perception and interaction of the recommendation in MusicBot. In general, compared with UC, SC yields higher perceived diversity and efficiency in looking for songs; combining UC and SC tends to increase user engagement. Both MS and DFC positively influence several key user experience (UX) metrics of MusicBot such as interest matching, perceived controllability, and intent to provide feedback.

Heterogeneous one-class collaborative filtering is an emerging and important problem in recommender systems, where two different types of one-class feedback, i.e., purchases and browses, are available as input data. The associated challenges include ambiguity of browses, scarcity of purchases, and heterogeneity arising from different feedback. In this article, we propose to model purchases and browses from a new perspective, i.e., users’ roles of mixer, browser and purchaser. Specifically, we design a novel transfer learning solution termed role-based transfer to rank (RoToR), which contains two variants, i.e., integrative RoToR and sequential RoToR. In integrative RoToR, we leverage browses into the preference learning task of purchases, in which we take each user as a sophisticated customer (i.e., mixer ) that is able to take different types of feedback into consideration. In sequential RoToR, we aim to simplify the integrative one by decomposing it into two dependent phases according to a typical shopping process. Furthermore, we instantiate both variants using different preference learning paradigms such as pointwise preference learning and pairwise preference learning. Finally, we conduct extensive empirical studies with various baseline methods on three large public datasets and find that our RoToR can perform significantly more accurate than the state-of-the-art methods.