The prosperous development of social e-commerce has spawned diverse recommendation demands, and accompanied a new recommendation paradigm, share recommendation. Significantly different from traditional binary recommendations (e.g., item recommendation and friend recommendation), share recommendation models ternary interactions among 〈 User, Item, Friend 〉 , which aims to recommend a most likely friend to a user who would like to share a specific item, progressively becoming an indispensable service in social e-commerce. Seamlessly integrating the social relations and purchase behaviours, share recommendation improves user stickiness and monetizes the user influence, meanwhile encountering three unique challenges: rich heterogeneous information, complex ternary interaction, and asymmetric share action. In this paper, we first study the share recommendation problem and propose a heterogeneous graph neural network based share recommendation model, called HGSRec. Specifically, HGSRec delicately designs a tripartite heterogeneous GNNs to describe the multifold characteristics of users and items, and then dynamically fuses them via capturing potential ternary dependency with a dual co-attention mechanism, followed by a transitive triplet representation to depict the asymmetry of share action and predict whether share action happens. Offline experiments demonstrate the superiority of the proposed HGSRec with significant improvements (11.7%-14.5%) over the state-of-the-arts, and online A/B testing on Taobao platform further demonstrates the high industrial practicability and stability of HGSRec.

The ability to transfer styles of texts or images, is an important measurement of the advancement of artificial intelligence (AI). However, the progress in language style transfer is lagged behind other domains, such as computer vision, mainly because of the lack of parallel data and reliable evaluation metrics. In response to the challenge of lacking parallel data, we explore learning style transfer from non-parallel data. We propose two models to achieve this goal. The key idea behind the proposed models is to learn separate content representations and style representations using adversarial networks. Considering the problem of lacking principle evaluation metrics, we propose two novel evaluation metrics that measure two aspects of style transfer: transfer strength and content preservation. We benchmark our models and the evaluation metrics on two style transfer tasks: paper-news title transfer, and positive-negative review transfer. Results show that the proposed content preservation metric is highly correlate to human judgments, and the proposed models are able to generate sentences with similar content preservation score but higher style transfer strength comparing to auto-encoder.

This paper implements a visual tool for the quick customization of virtual characters in 3D virtual worlds with a three-layer structure based on the Open Sim and Second Life platforms and "Knowledge" theory. "Knowledge" refers to specific controlling conditions and the corresponding behaviors of virtual characters. The three-layer structure is divided into the data layer, entity layer, and implementation layer. The data layer is used to store the data needed by the virtual characters' login and the behavioral control, the entity layer enables the exchange of data with the data layer and passes parameters to the implementation layer, and the implementation layer allows for realizing the knowledge of virtual characters with the support of the API in the Open Metaverse open source framework. Moreover, visualization of the tool is implemented through the interfaces provided by the entity layer. With this tool, users can generate a group of virtual characters quickly and conveniently at any place in a certain virtual world and select a variety of knowledge for diverse virtual characters.

Through scene construction and intelligent interaction in a 3D virtual world environment, we developed the project "Confucius' Journey". Considering the problems in such applications, such as the lack of interaction and reduced effectiveness in representing the application purpose, we explored interactive objects and virtual human technology. In addition, we can verify the advantage of using the 3D platform via the experimental results.