Cross-domain sequential recommendation (CDSR) aims to predict users’ preferences by leveraging their historical interaction sequences across multiple domains. Recent approaches using large language models (LLMs) have shown promise in addressing the challenge of limited overlapped users by generating pseudo cross-domain interactions for non-overlapped users. However, current methods employ static prompt templates that fail to capture the nuanced differences between users’ cross-domain preferences. This paper introduces a novel User-Adaptive LLM prompt template engineering (UA-PTE) framework that dynamically tailors LLM prompts based on individual user interaction patterns. Our approach features three key components: (1) User Embedding Conditioned Prompts that incorporate user-specific information into prompt templates, (2) Domain Relationship Templates that capture different types of cross-domain relationships, and (3)a Prompt Template Selection Network that identifies appropriate templates for each user. Extensive experiments on two public datasets demonstrate that our approach significantly outperforms state-of-the-art methods, particularly for users with unique cross-domain behavioral patterns.
As students practice a specific skill in a computerized learning environment, it is helpful to provide them with a clear goal and to continuously display their progress toward that goal. We propose a formulation of this goal as “sufficient skillful effort,” contrasting it with the traditional mastery learning perspective. We present an overview of previously proposed progress measures, discussing them using formal notation in a basic setting. We also introduce a novel method based on a variable-speed progress measure that leverages a Hidden Markov Model estimate of skill. To support analysis, we describe a simulation framework for exploring progress measures and use it to examine the properties of the studied methods. Finally, we discuss practical extensions, such as accounting for typical item response times and ensuring robustness against rapid guessing. Our proposals and discussions are informed by experience with designing and applying a progress measure in a widely used learning platform.
Personalized driving assistance systems for older drivers are promising tools for decreasing the cognitive load and promoting safe driving. However, the types of assistance that are effective for various drivers with specific characteristics are unclear. Our goal is to develop personalized driving assistance systems that adaptively provide support on the basis of driver characteristics. This pilot study focuses on driving support at stop sign intersections, and the interactions between types of support and driver characteristics on driving behavior are explored. First, we collected driving behavior data and driver characteristics information from 38 participants ( M_Age = 59.3, SD_Age = 15.1, Range_Age=19-83 ). They performed driving sessions on a simulator with driving support that differed in terms of its content and the amount of information provided. To examine how effective support differs depending on driver characteristics, we built regression models to explain driving behaviors considering the interactions between driver characteristics and support types. The interaction terms improved the adjusted R^2 by 0.08 to 0.32, indicating that driver characteristics were related to the differences in the support effect. Additionally, we employed a machine learning algorithm to uncover findings about effective support. In particular, drivers with cognitive decline tended to exhibit safe driving behaviors when provided with instructive support. In contrast, neurotic drivers seemed to have trouble processing the information provided by the driving support system. These findings reveal the importance of accounting for a wide range of driver characteristics for the personalization of support.
Gamified stress management applications often employ one-size-fits-all designs that overlook individual motivational differences, limiting their potential effectiveness. This study addresses this gap through a comprehensive three-stage investigation. First, we built on previous research that systematically reviewed stress management mobile interventions from 2003 to 2024, identifying the top ten evidence-based persuasive strategies. Second, we developed prototypes that embed these strategies within a gamified mobile application, iteratively refining them based on expert evaluations. Third, we evaluated the prototypes through a large-scale online study (n = 517), assessing perceived persuasiveness and examining the influence of HEXAD gamification user types on the effectiveness of ten persuasive strategies. Our findings revealed that while all ten strategies were effective, goal setting emerged as the most persuasive, followed by self-monitoring and rewards. Notably, preferences for specific strategies varied across gamification user types. For instance, goal setting demonstrated broad appeal but was not effective for Free Spirits, whereas suggestion showed differing effectiveness for Disruptors and Socializers compared to other user types. Our research contributes to the field of gamification and human–computer interaction by providing (1) empirical evidence of how HEXAD gamification user types predict persuasive strategy effectiveness in stress management apps, (2) identification and evaluation of the ten most prevalent persuasive strategies with universal and user-specific effectiveness patterns, and (3) actionable design guidelines for both broad implementation and HEXAD-tailored personalization. This work bridges the research in gamification, human–computer interaction, and mental health by providing theoretical insights and practical tools for moving beyond generic designs towards truly user-centred, persuasive systems in gamified mental health interventions.
Prior research has highlighted the need to balance accuracy with other exploration-oriented system objectives, such as novelty, serendipity, and diversity. However, relatively little has been done to investigate how real users perceive and experience this trade-off in music recommender systems. To address this gap, we conducted a user experiment study in which participants interacted with two contrasting music discovery tools: a seed-based discovery interface designed for accuracy, and Spotify’s Discover Weekly, which prioritizes exploration. One hundred and forty-four Spotify users completed the study in which they were asked to evaluate tracks/playlists resulting from the two music discovery tools. The results indicated that Discover Weekly performed significantly better in exploration-related measures, including diversity, novelty, and serendipity, and fewer previously known tracks. However, Discover Weekly performed significantly worse in accuracy, as measured by average track rating. In addition, overall satisfaction with the playlist is significantly influenced by both the perceived diversity of the playlist and the number of known tracks. To better understand individual differences in navigating this trade-off, we examined four music preference characteristics: music involvement, music self-identity, preference for diversity, and openness to novelty. These traits, measured via a questionnaire, allowed us to investigate their roles in moderating the effectiveness of these tools. Music involvement and music self-identity were positively associated with track ratings, regardless of the tool used. Importantly, individuals who consider music central to their self-identity demonstrated greater appreciation for the recommendations provided by Discover Weekly. Structural equation modeling further showed that music self-identity moderated users’ evaluations of Discover Weekly, such that individuals with higher music self-identity reported greater satisfaction with its recommendations, partly by attenuating the negative impact of reduced familiarity on perceived accuracy. Our findings highlight the need to balance familiarity and novelty and the importance of incorporating psychological factors into personalization strategies to enhance user satisfaction.
