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1. LuckyFind: Leveraging Surprise to Improve User Satisfaction and Inspire Curiosity in a Recommender System

   https://doi.org/10.1145/3406522.3446017  

   Niu, Xi; Al-Doulat, Ahmad (March 2021, CHIIR '21: Proceedings of the 2021 ACM SIGIR Conference on Human Information Interaction and Retrieval)

   null (Ed.)

   The growing amount of online information today has increased opportunity to discover interesting and useful information. Various recommender systems have been designed to help people discover such information. No matter how accurately the recommender algorithms perform, users’ engagement with recommended results has been complained being less than ideal. In this study, we touched on two human-centered objectives for recommender systems: user satisfaction and curiosity, both of which are believed to play roles in maintaining user engagement and sustain such engagement in the long run. Specifically, we leveraged the concept of surprise and used an existing computational model of surprise to identify relevantly surprising health articles aiming at improving user satisfaction and inspiring their curiosity. We designed a user study to first test the validity of the surprise model in a health news recommender system, called LuckyFind. Then user satisfaction and curiosity were evaluated. We find that the computational surprise model helped identify surprising recommendations at little cost of user satisfaction. Users gave higher ratings on interestingness than usefulness for those surprising recommendations. Curiosity was inspired more for those individuals who have a larger capacity to experience curiosity. Over half of the users have changed their preferences after using LuckyFind, either discovering new areas, reinforcing their existing interests, or stopping following those they did not want anymore. The insights of the research will make researchers and practitioners rethink the objectives of today’s recommender systems as being more human-centered beyond algorithmic accuracy. 

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2. Designing and Evaluating Interactive Tools for a Robot Hand Collection

   https://doi.org/10.1145/3568294.3580099  

   Abou_Chahine, Ramzi; Padmanabhuni, Sid; Fazli, Pooyan; Seifi, Hasti (March 2023, Association for Computing Machinery)

   Recent robot collections provide various interactive tools for users to explore and analyze their datasets. Yet, the literature lacks data on how users interact with these collections and which tools can best support their goals. This late-breaking report presents preliminary data on the utility of four interactive tools for accessing a collection of robot hands. The tools include a gallery and similarity comparison for browsing and filtering existing hands, a prediction tool for estimating user impression of hands (e.g., humanlikeness), and a recommendation tool suggesting design features (e.g., number of fingers) for achieving a target user impression rating. Data from a user study with 9 novice robotics researchers suggest the users found the tools useful for various tasks and especially appreciated the gallery and recommendation functionalities for understanding the complex relationships of the data. We discuss the results and outline future steps for developing interface design guidelines for robot collections. 

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3. Online Recognition of Bimanual Coordination Provides Important Context for Movement Data in Bimanual Teleoperated Robots

   Boehm, J.; Fey, N.; Majewicz Fey, A. (September 2021, IEEE International Conference on Robotics and Automation)

   null (Ed.)

   An important problem in designing human-robot systems is the integration of human intent and performance in the robotic control loop, especially in complex tasks. Bimanual coordination is a complex human behavior that is critical in many fine motor tasks, including robot-assisted surgery. To fully leverage the capabilities of the robot as an intelligent and assistive agent, online recognition of bimanual coordination could be important. Robotic assistance for a suturing task, for example, will be fundamentally different during phases when the suture is wrapped around the instrument (i.e., making a c- loop), than when the ends of the suture are pulled apart. In this study, we develop an online recognition method of bimanual coordination modes (i.e., the directions and symmetries of right and left hand movements) using geometric descriptors of hand motion. We (1) develop this framework based on ideal trajectories obtained during virtual 2D bimanual path following tasks performed by human subjects operating Geomagic Touch haptic devices, (2) test the offline recognition accuracy of bi- manual direction and symmetry from human subject movement trials, and (3) evalaute how the framework can be used to characterize 3D trajectories of the da Vinci Surgical System’s surgeon-side manipulators during bimanual surgical training tasks. In the human subject trials, our geometric bimanual movement classification accuracy was 92.3% for movement direction (i.e., hands moving together, parallel, or away) and 86.0% for symmetry (e.g., mirror or point symmetry). We also show that this approach can be used for online classification of different bimanual coordination modes during needle transfer, making a C loop, and suture pulling gestures on the da Vinci system, with results matching the expected modes. Finally, we discuss how these online estimates are sensitive to task environment factors and surgeon expertise, and thus inspire future work that could leverage adaptive control strategies to enhance user skill during robot-assisted surgery. 

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4. Review-Based Cross-Domain Collaborative Filtering: A Neural Framework

   Doan, T.N; Sahebi, S. (January 2019, Proceedings of the Third Workshop on Recommendation in Complex Scenarios)

   Cross-domain collaborative filtering recommenders exploit data from other domains (e.g., movie ratings) to predict users’ interests in a different target domain (e.g., suggest music). Most current cross-domain recommenders focus on modeling user ratings but pay limited attention to user reviews. Additionally, due to the complexity of these recommender systems, they cannot provide any information to users to support user decisions. To address these challenges, we propose Deep Hybrid Cross Domain (DHCD) model, a cross-domain neural framework, that can simultaneously predict user ratings, and provide useful information to strengthen the suggestions and support user decision across multiple domains. Specifically, DHCD enhances the predicted ratings by jointly modeling two crucial facets of users’ product assessment: ratings and reviews. To support decisions, it models and provides natural review-like sentences across domains according to user interests and item features. This model is robust in integrating user rating and review information from more than two domains. Our extensive experiments show that DHCD can significantly outperform advanced baselines in rating predictions and review generation tasks. For rating prediction tasks, it outperforms cross-domain and single-domain collaborative filtering as well as hybrid recommender systems. Furthermore, our review generation experiments suggest an improved perplexity score and transfer of review information in DHCD. 

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5. Evaluating Grasping Visualizations and Control Modes in a VR Game

   https://doi.org/10.1145/3486582  

   Adkins, Alex; Lin, Lorraine; Normoyle, Aline; Canales, Ryan; Ye, Yuting; Jörg, Sophie (October 2021, ACM Transactions on Applied Perception)

   A primary goal of the Virtual Reality ( VR ) community is to build fully immersive and presence-inducing environments with seamless and natural interactions. To reach this goal, researchers are investigating how to best directly use our hands to interact with a virtual environment using hand tracking. Most studies in this field require participants to perform repetitive tasks. In this article, we investigate if results of such studies translate into a real application and game-like experience. We designed a virtual escape room in which participants interact with various objects to gather clues and complete puzzles. In a between-subjects study, we examine the effects of two input modalities (controllers vs. hand tracking) and two grasping visualizations (continuously tracked hands vs. virtual hands that disappear when grasping) on ownership, realism, efficiency, enjoyment, and presence. Our results show that ownership, realism, enjoyment, and presence increased when using hand tracking compared to controllers. Visualizing the tracked hands during grasps leads to higher ratings in one of our ownership questions and one of our enjoyment questions compared to having the virtual hands disappear during grasps as is common in many applications. We also confirm some of the main results of two studies that have a repetitive design in a more realistic gaming scenario that might be closer to a typical user experience. 

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