Research talk: Extracting pragmatics from content interactions to improve enterprise recommendations
0Speaker: Jennifer Neville, Senior Principal Researcher, Microsoft Research Redmond
Data trails, recording the way that people interact with content and with each other in an enterprise, are a source of linguistic pragmatics (cues to language meaning implied by social interactions) that can be used to improve search and recommendation, particularly in tail scenarios. Graph ML methods are uniquely positioned to be able to learn from these data trails by jointly considering multiple modalities of interaction data, and collectively propagating pragmatics across teams/organizations. In this talk, we will present our recent research incorporating these signals into GNN methods—to learn jointly from content and relational interactions.
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