Interactions Of Individual Users Research Articles

Social-Enhanced Attentive Group Recommendation

With the proliferation of social networks, group activities have become an essential ingredient of our daily life. A growing number of users share their group activities online and invite their friends to join in. This imposes the need of an in-depth study on the group recommendation task, i.e., recommending items to a group of users. Despite its value and significance, group recommendation remains an unsolved problem due to 1) the weights of group members are crucial to the recommendation performance but are rarely learnt from data; 2) social followee information is beneficial to understand users' preferences but is rarely considered; and 3) user-item interactions are helpful to reinforce the performance of group recommendation but are seldom investigated. Toward this end, we devise neural network-based solutions by utilizing the recent developments of attention network and neural collaborative filtering (NCF). First of all, we adopt an attention network to form the representation of a group by aggregating the group members' embeddings, which allows the attention weights of group members to be dynamically learnt from data. Second, the social followee information is incorporated via another attention network to enhance the representation of individual user, which is helpful to capture users' personal preferences. Third, considering that many online group systems also have abundant interactions of individual users on items, we further integrate the modeling of user-item interactions into our method. Through this way, the recommendation for groups and users can be mutually reinforced. Extensive experiments on the scope of both macro-level performance comparison and micro-level analyses justify the effectiveness and rationality of our proposed approaches.

Contextualizing and capturing individual user interactions in shared iTV environments

Advances in Interactive TV (iTV) technology have enabled users to actively interact with the TV instead of just passively watching it. Associating the individual user interactions with contextual data (e.g., date, time, current channel, and people around) may reveal important information about user interests regarding the iTV content. However, capturing individual data is a difficult task since it lacks a proper mechanism to identify viewers while using the iTV. In a typical TV environment, a viewer has only a conventional remote control (RC) device being shared by other viewers, which makes it difficult to distinguish the events performed by each user. This paper presents a novel approach that facilitates the capture of contextualized and individualized data while users interact with the iTV content by using mobile devices as second screen interfaces. In contrast with conventional RCs, mobile devices are personal and typically present advanced computing and communication capabilities that makes it possible to distinguish each viewer and allows the capture of individual and contextualized interactions. The data generated in those interactions may be interpreted by specific algorithms becoming useful information for TV service providers (TSPs) enhancing TV-based services, e.g., advertising and personalization. An experimental prototype was developed as a proof of concept for the mechanism proposed in this paper. The prototype consists of an application that allows to capture and contextualize interactions of three iTV related events: channel change, sound volume change, and content evaluation.

A Unified Framework for Individualized Avatar-Based Interactions

This paper presents a framework to interactively control avatars in remote environments. The system, called AMITIES, serves as the central component that connects people controlling avatars (inhabiters), various manifestations of these avatars (surrogates), and people interacting with these avatars (participants). A multiserver–client architecture, based on a low-demand network protocol, connects the participant environment(s), the inhabiter station(s), and the avatars. A human-in-the-loop metaphor provides an interface for remote operation, with support for multiple inhabiters, multiple avatars, and multiple participants. Custom animation blending routines and a gesture-based interface provide inhabiters with an intuitive avatar control paradigm. This gesture control is enhanced by genres of program-controlled behaviors that can be triggered by events or inhabiter choices for individual or groups of avatars. This mixed (agency and gesture-based) control paradigm reduces the cognitive and physical loads on the inhabiter while supporting natural bidirectional conversation between participants and the virtual characters or avatar counterparts, including ones with physical manifestations, for example, robotic surrogates. The associated system affords the delivery of personalized experiences that adapt to the actions and interactions of individual users, while staying true to each virtual character's personality and backstory. In addition to its avatar control paradigm, AMITIES provides processes for character and scenario development, testing, and refinement. It also has integrated capabilities for session recording and event tagging, along with automated tools for reflection and after-action review. We demonstrate effectiveness by describing an instantiation of AMITIES, called TeachLivE, that is widely used by colleges of education to prepare new teachers and provide continuing professional development to existing teachers. Finally, we show the system's flexibility by describing a number of other diverse applications, and presenting plans to enhance capabilities and application areas.

Influence of standardized activities on validity of Assessment of Capacity for Myoelectric Control

The Assessment of Capacity for Myoelectric Control (ACMC) is an observation-based clinical tool that evaluates ability to control a myoelectric prosthetic hand during bimanual activities. Two validity aspects were investigated: potential bias interaction between prosthesis users and activities performed during assessment, and potential bias interaction between activities and different user characteristics (sex or prosthetic side). Six activities were standardized for the ACMC. Upper-limb myoelectric prosthesis users (47 congenital, 11 acquired; 31 male, 27 female, average age 19.9 yr) performed three standardized activities, each on one occasion. Bias-interaction analysis in the many-facet Rasch model identified inconsistent patterns in the interactions of individual users and activity facets and between activities and user characteristics. The standardized activities had no significant influence on measures of user ability. The activities functioned similarly across both sexes (p-value greater than or equal to0.12) and across both prosthetic sides in persons with upper-limb reduction deficiency (p-value greater than or equal to0.50) and persons with acquired amputation (p-value greater than or equal to0.13). The results provide evidence for the validity of the ACMC across the standardized activities and support use of the ACMC in prosthesis users of both sexes and prosthetic sides. The newly standardized activities are recommended for future ACMC use.