Toward Faceted Skill Recommendation in Intelligent Personal Assistants

Manveer Kalirai, Alex C. Williams, Anastasia Kuzminykh

Abstract: Research continuously shows that, despite the wide range of skills developed for Intelligent Personal Assistants (IPAs), users tend to engage with only a small number of them. One reason for this discrepancy is the issue of skill discoverability, which is commonly addressed through conversational recommendations. Current recommendation strategies, however, are limited due to information asymmetry, lack of interactivity, and an underdeveloped understanding of appropriate grouping of available skills. In this paper, we explore opportunities for interactive faceted skill recommendations using voice interfaces. Through an open card sort user study and semi-structured interviews, we identify and describe five facets driving users’ natural grouping of IPA skills (Thematic, Procedural, Cross-system, Environmental, and Recipient), and demonstrate the need for simultaneous support of these facets. We then discuss the implications of these findings for advancing the discoverability of IPA skills through the design of interactive conversational recommendations.