Faster Responses Are Better Responses: Introducing Incrementality into Sociable Virtual Personal Assistants

Tsai, Vivian; Baumann, Timo; Pecune, Florian; Cassell, Justine

doi:10.1007/978-981-13-9443-0_10

Cited by 11 publications

(5 citation statements)

References 8 publications

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“…When presenting recommendations, both the modality (e.g., text vs. image) and the organization (single item vs. list of items) play a role [19]. Additionally, minimizing latencies within the system improves users' perceived quality of the recommendation [34]. The faster a recommendation is delivered, the more relevant it is perceived.…”

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confidence: 99%

A Model of Social Explanations for a Conversational Movie Recommendation System

Pecune

Murali

Tsai

et al. 2019

Proceedings of the 7th International Conference on Human-Agent Interaction

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A critical aspect of any recommendation process is explaining the reasoning behind each recommendation. These explanations can not only improve users' experiences, but also change their perception of the recommendation quality. This work describes our human-centered design for our conversational movie recommendation agent, which explains its decisions as humans would. After exploring and analyzing a corpus of dyadic interactions, we developed a computational model of explanations. We then incorporated this model in the architecture of a conversational agent and evaluated the resulting system via a user experiment. Our results show that social explanations can improve the perceived quality of both the system and the interaction, regardless of the intrinsic quality of the recommendations. CCS CONCEPTS • Human-centered computing → Natural language interfaces.

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mentioning

confidence: 99%

A Model of Social Explanations for a Conversational Movie Recommendation System

Pecune

Murali

Tsai

et al. 2019

Proceedings of the 7th International Conference on Human-Agent Interaction

Self Cite

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“…Other factors might have an impact on users’ experience with recommender systems, and can therefore influence users’ perception of a recommended item ( Jugovac and Jannach, 2017 ). The way the recommendations are presented ( Nanou et al, 2010 ) as well as the system’s embodiment ( Herse et al, 2018 ), latency ( Tsai et al, 2019 ), and sentence length ( Pecune et al, 2018 ) might all affect users’ perception.…”

Section: Related Workmentioning

confidence: 99%

Designing Persuasive Food Conversational Recommender Systems With Nudging and Socially-Aware Conversational Strategies

2022

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Unhealthy eating behavior is a major public health issue with serious repercussions on an individual’s health. One potential solution to overcome this problem, and help people change their eating behavior, is to develop conversational systems able to recommend healthy recipes. One challenge for such systems is to deliver personalized recommendations matching users’ needs and preferences. Beyond the intrinsic quality of the recommendation itself, various factors might also influence users’ perception of a recommendation. In this paper, we present Cora, a conversational system that recommends recipes aligned with its users’ eating habits and current preferences. Users can interact with Cora in two different ways. They can select pre-defined answers by clicking on buttons to talk to Cora or write text in natural language. Additionally, Cora can engage users through a social dialogue, or go straight to the point. Cora is also able to propose different alternatives and to justify its recipes recommendation by explaining the trade-off between them. We conduct two experiments. In the first one, we evaluate the impact of Cora’s conversational skills and users’ interaction mode on users’ perception and intention to cook the recommended recipes. Our results show that a conversational recommendation system that engages its users through a rapport-building dialogue improves users’ perception of the interaction as well as their perception of the system. In the second evaluation, we evaluate the influence of Cora’s explanations and recommendation comparisons on users’ perception. Our results show that explanations positively influence users’ perception of a recommender system. However, comparing healthy recipes with a decoy is a double-edged sword. Although such comparison is perceived as significantly more useful compared to one single healthy recommendation, explaining the difference between the decoy and the healthy recipe would actually make people less likely to use the system.

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“…However, these results would not be satisfactory in case of a dialogue, since additional processing related to user input and more complex response generation would be required in that case. Therefore, optimization techniques would be required, similar to case described in [33].…”

Section: Case Study -Speech-enabled Application In Amentioning

confidence: 99%

N-gram Based Croatian Language Network: Application in a Smart Environment

Soic

Vuković

2022

JCOMSS

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In the field of natural language processing, language networks represent a method for observing linguistic units and their interactions in different linguistic contexts. This paper uses the previously presented Croatian language network for building a solution capable of generating spoken notifications in Croatian language. The novelty of this paper is that it proposes an approach for generating spoken notifications in smart environments by combining specialized services that enable interaction with the environment and human users. The process employed for generating spoken notifications is described in detail. Also, a novel contribution of this paper is the case-study evaluation of the proposed approach in a smart home environment.

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Faster Responses Are Better Responses: Introducing Incrementality into Sociable Virtual Personal Assistants

Cited by 11 publications

References 8 publications

A Model of Social Explanations for a Conversational Movie Recommendation System

A Model of Social Explanations for a Conversational Movie Recommendation System

Designing Persuasive Food Conversational Recommender Systems With Nudging and Socially-Aware Conversational Strategies

N-gram Based Croatian Language Network: Application in a Smart Environment

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