Multimodal channel recommendation system
The process of collaboration in large organizations involves multiple means of communication between employees, teams, and departments, such as group meetings, personal chats, and public channels.
Channels are often related to specific topics, such as a project or an event, and may serve as a source of information for anyone interested in it.
However, messenger workspaces in large organizations often have so many channels that employees who can benefit from certain channels may be unaware of their existence. Thus, recommendation systems suggesting channels without any query specified by the user may help them discover relevant information.
The more information is available about users, the more accurate recommendations we can achieve. Moreover, combining multimodal data from all collaboration platforms could give a better representation of social and technical communication inside organizations. For example, knowledge of organizational structure could trigger position-based recommendations, or, in the case of IT companies, projects and source code could provide information on users' professional interests. However, such data is usually distributed across multiple platforms and is difficult to collect, so most of the existing approaches only use unimodal data.
The objective of this study is to build a channel suggestion system working with multimodal data and analyze the effects of multimodality on quality of the recommendations.