Virtual reality head-mounted displays affect sidetone perception.

The purpose of this study was to investigate whether head-mounted displays (HMDs) change the sidetone to an auditory perceivable extent. Impulse responses (IRs) were recorded using a dummy head wearing a HMD (IRtest) and compared to IRs measured without HMD (IRref). Ten naive listeners were tested on their ability to discriminate between the IRtest and IRref using convolved speech signals. The spectral analysis showed that the HMDs decreased the spectral energy of the sidetone around 2000-4500 Hz. Most listeners were able to discriminate between the IRs. It is concluded that HMDs change the sidetone to a small but perceivable extent.

Exploiting Implicit Influence From Information Propagation for Social Recommendation.

Social recommender systems have attracted a lot of attention from academia and industry. On social media, users' ratings and reviews can be observed by all users, and have implicit influence on their future ratings. When these users make subsequent decisions about an item, they may be affected by existing ratings on the item. Thus, implicit influence propagates among the users who rated the same items, and it has significant impact on users' ratings. However, implicit influence propagation and its effect on recommendation rarely have been studied. In this article, we propose an information propagation-based social recommendation method (SoInp) and model the implicit user influence from the perspective of information propagation. The implicit influence is inferred from ratings on the same items. We investigate the concrete effect of implicit user influence in the propagation process and introduce it into recommender systems. Furthermore, we incorporate the implicit user influence and explicit trust information in the matrix factorization framework. To demonstrate the performance, we conduct comprehensive experiments on real-world datasets to compare the proposed method with the state-of-the-art models. The results indicate that SoInp makes notable improvements in rating prediction.