Random walk based distributed representation learning and prediction on Social Networking Services

Abstract Social Networking Services (SNSs) provide online platforms for users with two kinds of behavior: user-user social behavior (e.g., following a user, making friends with others) and user-item consumption behavior (e.g., rating, showing likeness, clicking, giving thumbs up to items). With the increasing popularity of SNSs and demand for SNS features, predicting potential social links and recommending preferable items to users have become two hot research lines. However, previous works either modeled just one of these two kinds of behaviors in isolation or only considered the observed user behavior data. In fact, social scientists have long recognized that the user-user and user-item behaviors have a mutual reinforcement effect. On the one hand, the two behaviors have correlations, and they can influence each other. On the other hand, due to the sparsity of the observed user behavior data, the user behavior prediction performance is far from satisfactory, although, using two types of behavioral data at the same time can mitigate the sparsity problem. These two problems remains open: how to better model the correlation of user-user social and user-item consumption activities and how to mitigate the data sparsity issue. In this paper, we propose a random walk based distributed representation learning model to jointly predict these behaviors on SNSs. Specifically, we first construct a joint behavior graph that combines the two behaviors, with the edges denoting the sparse observed user behavior data. Then, we adopt a random walk to capture higher-order relationships between users and items. After that, we utilize a distributed learning approach to embed both users and items into a latent space. In this way, the behavior prediction tasks are transformed into similarity calculations in the latent space. Finally, extensive experimental results using two real-world datasets demonstrate the effectiveness of our proposed approach on the two behavior prediction tasks.