Vote Goat: Conversational Movie Recommendation

Conversational search and recommendation systems that use natural language interfaces are an increasingly important area raising a number of research and interface design questions. Despite the increasing popularity of digital personal assistants, the number of conversational recommendation systems is limited and their functionality basic. In this demonstration we introduce Vote Goat, a conversational recommendation agent built using Google's DialogFlow framework. The demonstration provides an interactive movie recommendation system using a speech-based natural language interface. The main intents span search and recommendation tasks including: rating movies, receiving recommendations, retrieval over movie metadata, and viewing crowdsourced statistics. Vote Goat uses gamification to incentivize movie voting interactions with the 'Greatest Of All Time' (GOAT) movies derived from user ratings. The demo includes important functionality for research applications with logging of interactions for building test collections as well as A/B testing to allow researchers to experiment with system parameters

Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

How Voice Can Change Customer Satisfaction: A Comparative Analysis between E-Commerce and Voice Commerce

Voice commerce is a newly evolving e-commerce channel where consumers communicate with dedicated systems on smart speakers or other devices using their voice, in order to find products. This paper comparatively investigates factors for customers’ satisfaction in voice commerce and ecommerce. Being the first study to scientifically analyze customer satisfaction factors in voice commerce and compare them with e-commerce, we conducted a survey with 178 consumers and used structural equation modeling for statistical hypotheses testing. The results show, that consumers have higher expectations in convenience for voice commerce than they have for ecommerce. Transaction process efficiency significantly influences satisfaction in voice commerce, but not in e-commerce. This research provides implications for future research on voice commerce strategy and system design

ECA gesture strategies for robust SLDSs

This paper explores the use of embodied conversational agents (ECAs) to improve interaction with spoken language dialogue systems (SLDSs). For this purpose we have identified typical interaction problems with SLDSs and associated with each of them a particular ECA gesture or behaviour. User tests were carried out dividing the test users into two groups, each facing a different interaction metaphor (one with an ECA in the interface, and the other implemented only with voice). Our results suggest user frustration is lower when an ECA is present in the interface, and the dialogue flows more smoothly, partly due to the fact that users are better able to tell when they are expected to speak and whether the system has heard and understood. The users’ overall perceptions regarding the system were also affected, and interaction seems to be more enjoyable with an ECA than without it