A Model of Social Explanations for a Conversational Movie Recommendation System

Pecune, Florian; Murali, S.S.; Tsai, Vivian; Matsuyama, Yoichi; Cassell, Justine

doi:10.1145/3349537.3351899

Cited by 42 publications

(34 citation statements)

References 35 publications

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“…Although there have been a series of databases that are built from movies (Tapaswi et al, 2016 ; Chu and Roy, 2017 ; Pecune et al, 2019 ), few, if any, have been created to train children's conversational agents. For example, MovieQA (Tapaswi et al, 2016 ) was created to answer questions about the content of movies by using ~15,000 multiple choice question answers, but not to engage in social conversations.…”

Section: Generating Data For Children's Conversational Agentsmentioning

confidence: 99%

A Perspective on Building Ethical Datasets for Children's Conversational Agents

Bailey

Patel

Gurari

2021

Front. Artif. Intell.

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Artificial intelligence (AI)-powered technologies are becoming an integral part of youth's environments, impacting how they socialize and learn. Children (12 years of age and younger) often interact with AI through conversational agents (e.g., Siri and Alexa) that they speak with to receive information about the world. Conversational agents can mimic human social interactions, and it is important to develop socially intelligent agents appropriate for younger populations. Yet it is often unclear what data are curated to power many of these systems. This article applies a sociocultural developmental approach to examine child-centric intelligent conversational agents, including an overview of how children's development influences their social learning in the world and how that relates to AI. Examples are presented that reflect potential data types available for training AI models to generate children's conversational agents' speech. The ethical implications for building different datasets and training models using them are discussed as well as future directions for the use of social AI-driven technology for children.

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Section: Generating Data For Children's Conversational Agentsmentioning

confidence: 99%

A Perspective on Building Ethical Datasets for Children's Conversational Agents

Bailey

Patel

Gurari

2021

Front. Artif. Intell.

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“…Yoo et al (2012) propose that credibility, likeability, friendliness, humor, and other language styles are significant factors for persuasive recommendations. Pecune et al (2019) has studied modeling social explanation for movie rec-ommendation, such as personal opinion and personal experience. Häubl and Murray (2003) find that more information on recommendation may help consumers make better purchase decisions, but leave them overwhelmed with the abundant information.…”

Section: Related Workmentioning

confidence: 99%

“…Sociable conversational agents build rapport with users, in order to gain trust and favor from them. Social science researchers believe that the rapport influence a more persuasive recommendation to successfully suggest an item that satisfies user needs (Yoo et al, 2012;Gkika and Lekakos;Pecune et al, 2019;Gretzel and Fesenmaier, 2006).…”

Section: Introductionmentioning

confidence: 99%

INSPIRED: Toward Sociable Recommendation Dialog Systems

Hayati¹,

Kang²,

Zhu³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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In recommendation dialogs, humans commonly disclose their preference and make recommendations in a friendly manner. However, this is a challenge in developing a sociable recommendation dialog system, due to the lack of dialog dataset annotated with such sociable strategies. Therefore, we present INSPIRED, a new dataset of 1,001 human-human dialogs for movie recommendation with measures for successful recommendations. To better understand how humans make recommendations in communication, we design an annotation scheme related to recommendation strategies based on social science theories and annotate these dialogs. Our analysis shows that sociable recommendation strategies, such as sharing personal opinions or communicating with encouragement, more frequently lead to successful recommendations. Based on our dataset, we train end-to-end recommendation dialog systems with and without our strategy labels. In both automatic and human evaluation, our model with strategy incorporation outperforms the baseline model. This work is a first step for building sociable recommendation dialog systems with a basis of social science theories 1 .

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“…( 1) i interest rate = 0.6 * (i basket popularity )/totalitem + 0.5 * (i browse popularity )/totalitem + 0.9 * (i purchase popularity )/totalitem + 0.8 * (u number of previous sessions ) + 0.5 * (u number of pages viewed ) + 0.9 * (u number of previous purchases )…”

Section: B Interest Level Mappingmentioning

confidence: 99%

“…It is also an important tool for increasing users' satisfaction by providing personalised recommendations. This has been applied to many domains including movie [1], music [2] and e-commerce [3], [4], and many e-commerce companies such as eBay and Amazon [5] also take advantage of RS.…”

Section: Introductionmentioning

confidence: 99%

Improving Session-Based Recommendation Adopting Linear Regression-Based Re-ranking

Esmeli

Bader-El-Den

Abdullahi

et al. 2020

2020 International Joint Conference on Neural Networks (IJCNN)

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Due to the increase in the importance of giving real-time recommendation to e-commerce users, session-based recommender systems become more popular. Session-based recommendation systems have the ability to adapt quickly to respond to changes in user interests and newly added items. The ranking is the core part of recommender systems regardless of recommender system type. Re-ranking is applied to recommender systems to have more personalised recommendations by considering contextawareness. In this paper, we proposed an approach to re-rank recommended items by using a linear regression model. In our approach, we use users' current session features and temporal features of recommended items to measure a user's interest level on a recommended item. We focus on having better recall and precision scores with fewer recommendations to able to prove the success of our re-ranking strategy. We conduct computational experiments on six real-world datasets and show that after applying re-ranking, we can get higher recall and precision scores. These results confirm that taking user interest level on an item in a session into account can improve the chance of getting correct items in top 5 recommendations.

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A Model of Social Explanations for a Conversational Movie Recommendation System

Cited by 42 publications

References 35 publications

A Perspective on Building Ethical Datasets for Children's Conversational Agents

A Perspective on Building Ethical Datasets for Children's Conversational Agents

INSPIRED: Toward Sociable Recommendation Dialog Systems

Improving Session-Based Recommendation Adopting Linear Regression-Based Re-ranking

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