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In social-enhanced recommendation systems such as Twitter and Weibo, users could get information from both personalized recommendation and social diffusion modules. In real-world scenarios, the user-group-user based social diffusion plays an essential role to efficiently broadcast information to groups of target users. Through this diffusion path, users first click items provided by the recommendation module, and then share the clicked items to the target user groups. Other users in the group can click the shared items, and return back to the recommendation module for more contents and related items. However, most social-enhanced recommendation systems merely focus on the recommendation module that they can directly influence, ignoring explicitly modeling and predicting for the social diffusion module. In this work, we propose a novel Group-based social diffusion (GSD) model, which aims to jointly optimize the click, share, and return stages in social-enhanced recommendation. We design a heterogeneous ternary graph neural network to jointly model the complex binary and ternary relations among users, items, and groups. We conduct extensive experiments and achieve significant improvements on all click, share, and return prediction tasks, and also achieve promising results on a new full-chain social impact prediction task. [ABSTRACT FROM AUTHOR]

Recommender systems can suffer from data sparsity and cold start issues. However, social networks, which enable users to build relationships and create different types of items, present an unprecedented opportunity to alleviate these issues. In this paper, we represent a social network as a star-structured hybrid graph centered on a social domain, which connects with other item domains. With this innovative representation, useful knowledge from an auxiliary domain can be transferred through the social domain to a target domain. Various factors of item transferability, including popularity and behavioral consistency, are determined. We propose a novel Hybrid Random Walk (HRW) method, which incorporates such factors, to select transferable items in auxiliary domains, bridge cross-domain knowledge with the social domain, and accurately predict user-item links in a target domain. Extensive experiments on a real social dataset demonstrate that HRW significantly outperforms existing approaches. [ABSTRACT FROM PUBLISHER]