Abstract: In some embodiments, holographically displaying items based on item compatibility with user account features may be facilitated. In some embodiments, first and second mobile devices may be detected as being within a proximity threshold of a holographic display device, where the first mobile device is associated with a first user account and the second mobile device is associated with a second user account. Based on the detection, (i) navigation information of the first and second mobile devices, (ii) return value information indicating respective return values associated with candidate items, and (iii) account feature compatibility information may be obtained and inputted into a machine learning model. Despite a first candidate item being incompatible with a second account feature set for the second mobile device, a first holographic representation of the first candidate item may be displayed.

Abstract: In some embodiments, holographic presentations despite using holographic displays having limited viewing angles may be facilitated. In some embodiments, a user may be detected via one or more sensors associated with a holographic display system, where the holographic display system being configured with a first viewing angle. Based on the detection of the user, a direction of the user may be determined. The first viewing angle of the holographic display system may also be determined. For example, the holographic display system may determine a current first viewing angle. Based on the direction of the user, the first viewing angle may be adjusted such that the user becomes within the adjusted first viewing angle of the holographic display system.

Abstract: Methods and systems comprising a first portion of a model that includes a model component that is trained to perform sentiment analysis based on training data for a plurality of users (e.g., what language, phrases, and/or responses the population at large uses). The first portion of the model also includes a model component that is trained to identify user intent based on the sentiment analysis that is specific to user groups. For example, the system first determines the likely context and/or meaning of communications of the user. The system then determines a likely intent of the user based on the likely context and/or meaning of communications (e.g., based on a correlation of the meaning of communications of the user and the intents of users corresponding to a user group of the user).