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Recently, conversational recommender system (CRS) has become an emerging and practical research topic. Most of the existing CRS methods focus on learning effective preference representations for users from conversation data alone. While, we take a new perspective to leverage historical interaction data for improving CRS. For this purpose, we propose a novel pre-training approach to integrating both item-based preference sequence (from historical interaction data) and attribute-based preference sequence (from conversation data) via pre-training methods. We carefully design two pre-training tasks to enhance information fusion between item- and attribute-based preference. To improve the learning performance, we further develop an effective negative sample generator which can produce high-quality negative samples. Experiment results on two real-world datasets have demonstrated the effectiveness of our approach for improving CRS., Comment: Accepted as CIKM short paper

Providing users with rich sensations is beneficial to enhance their immersion in Virtual Reality (VR) environments. Wetness is one such imperative sensation that affects users' sense of comfort and helps users adjust grip force when interacting with objects. Researchers have recently begun to explore ways to create wetness illusions, primarily on a user's face or body skin. In this work, we extended this line of research by creating wetness illusion on users' fingertips. We first conducted a user study to understand the effect of thermal and tactile feedback on users' perceived wetness sensation. Informed by the findings, we designed and evaluated a prototype---Mouille---that provides various levels of wetness illusions on fingertips for both hard and soft items when users squeeze, lift, or scratch it. Study results indicated that users were able to feel wetness with different levels of temperature changes and they were able to distinguish three levels of wetness for simulated VR objects. We further presented applications that simulated an ice cube, an iced cola bottle, and a wet sponge, etc, to demonstrate its use in VR.

This study proposes a tabletop projection device that can be implemented by combining physical objects with interactive projections. Users can interact on kraft papers using daily tools, such as marker pens, toothbrushes, colored blocks, and square wooden blocks. The input of the proposed device is a multifunction sensor, and the output is a tabletop projector. Using MagicPAPER, four types of interactions are implemented, namely drawing, gesture recognition, brushing, and building blocks. The abstract and poster discuss the design motivations and system descriptions of MagicPAPER.

Limb motion controlled robots have been developed and applied in many fields that affect every aspect of people's life. This paper presents a wearable massage robot named Anmoji, which is controlled by motions of a user's head, shoulders, and ankles. Anmoji is controlled in real time and able to provide functions including head massage, neck massage, back massage, leg massage, foot massage and multi-functional holder. Anmoji contributes a novel combination between massage and limb controlled robot, enabling a host of assistive interactions with the massage actions.

As the most common writing material in our daily life, paper is an important carrier of traditional painting, and it also has a more comfortable physical touch than electronic screens. In this study, we designed a shadow-art device for human-computer interaction called MagicPAPER, which is based on physical touch detection, gesture recognition, and reality projection. MagicPAPER consists of a pen, kraft paper, and several detection devices, such as AirBar, Kinect, LeapMotion, and WebCam. To make our MagicPAPER more interesting, we developed more than a dozen applications that allow users to experience and explore creative interactions on a desktop with a pen and a piece of paper. Results of our user study showed that MagicPAPER has received positive feedback from many different types of users, particularly children.

As the most common writing material in our daily life, paper is an important carrier of traditional painting, and it also has a more comfortable physical touch than electronic screens. In this study, we designed a shadow-art device for human--computer interaction called MagicPAPER, which is based on physical touch detection, gesture recognition, and reality projection. MagicPAPER consists of a pen, kraft paper, and several detection devices, such as AirBar, Kinect, LeapMotion, and WebCam. To make our MagicPAPER more interesting, we developed thirteen applications that allow users to experience and explore creative interactions on a desktop with a pen and a piece of paper.

Touch interaction is usually implemented on a computer screen or a projected surface. However, tactile quality of paper is attractive and intuitive for people to manipulate in traditional paintings and storytelling. In order to enable touch interaction on paper, we present MagicPAPER, a shadow-art device that consists of a pen, kraft paper, a projector, a gesture sensing device, and a software system. With the purpose of illustrating how MagicPAPER effectively integrates the art-design process with the computer and communication technology, we have developed eight interactive applications simply based on kraft paper and mark pen: the Dimension Door, Balls, Palette, Flat Cube, Piano, Color potion, Fast mask- changing, and Candle. Finally, we experimented with MagicPAPER and demonstrated its interactive effects by detecting the gesture of a finger and pen on kraft paper and projecting it into eight interactive applications. Our research results show that MagicPAPER successfully carries out paper-based touch interactions.