Generating Reliable Friends via Adversarial Training to Improve Social Recommendation Most of the recent studies of social recommendation assume that people share similar preferences with their friends and the online social relations are helpful in improving traditional recommender systems. However, this assumption is often untenable as the online social networks are quite sparse and a majority of users only have a small number of friends. Besides, explicit friends may not share similar interests because of the randomness in the process of building social networks. Therefore, discovering a number of reliable friends for each user plays an important role in advancing social recommendation. Unlike other studies which focus on extracting valuable explicit social links, our work pays attention to identifying reliable friends in both the observed and unobserved social networks. Concretely, in this paper, we propose an endtoend social recommendation framework based on Generative Adversarial Nets (GAN). The framework is composed of two blocks: a generator that is used to produce friends that can possibly enhance the social recommendation model, and a discriminator that is responsible for assessing these generated friends and ranking the items according to both the current user and her friendsu0027 preferences. With the competition between the generator and the discriminator, our framework can dynamically and adaptively generate reliable friends who can perfectly predict the current useru0027 preference at a specific time. As a result, the sparsity and unreliability problems of explicit social relations can be mitigated and the social recommendation performance is significantly improved. Experimental studies on realworld datasets demonstrate the superiority of our framework and verify the positive effects of the generated reliable friends.

To Be or Not to Be: Analyzing & Modeling Social Recommendation in Online Social Networks Firms are now considering to offer rewards to customers who recommend the firmsu0027 productsservices in online social networks (OSN). However, the pros and cons of such social recommendation scheme are still unclear. Thus, it is difficult for firms to design rewarding schemes. Via empirical analysis of data, we first identify key factors that affect the spreading of a firmu0027s product in OSNs. These findings enable us to develop an accurate (i.e., with a high validation accuracy) mathematical model on social recommendations. In particular, our model captures how users decide whether to recommend an item, which is a key factor but often ignored by previous social recommendation models such as the Independent Cascade model. We also design algorithms to infer model parameters. Using these parameters in our model, we uncover conditions when social recommendation can (or cannot) improves a firmu0027s profit. These conditions help a firm to design rewarding schemes. Finally, we extend our model to an online setting and design reinforcement learning algorithms for a firm to dynamically optimize its rewarding schemes.