A Socially-Aware Conversational Recommender System for Personalized Recipe Recommendations

Pecune, Florian; Callebert, Lucile; Marsella, Stacy

doi:10.1145/3406499.3415079

Cited by 38 publications

(19 citation statements)

References 32 publications

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“…As in ( Burke, 2002 ; Pecune et al, 2020a ), Cora relies on a hybrid approach combining knowledge-based (KB) and collaborative filtering techniques (CF) for this experiment. The recommendation process has three steps.…”

Section: Experiments 2: Evaluating the Influence Of Cora’s Nudges And Explanationsmentioning

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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Section: Experiments 2: Evaluating the Influence Of Cora’s Nudges And Explanationsmentioning

confidence: 99%

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

2022

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“…A multi-view method is proposed in Chen et al (2020b) for the explainable conversational recommendation. The work of Pecune et al (2020) builds a socially aware CR system engaging its users through a rapportbuilding dialogue to improve users' perception.…”

Section: Related Workmentioning

confidence: 99%

RevCore: Review-Augmented Conversational Recommendation

Yu¹,

Bao²,

Yan³

et al. 2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Existing conversational recommendation (CR) systems usually suffer from insufficient item information when conducted on short dialogue history and unfamiliar items. Incorporating external information (e.g., reviews) is a potential solution to alleviate this problem. Given that reviews often provide a rich and detailed user experience on different interests, they are potential ideal resources for providing high-quality recommendations within an informative conversation. In this paper, we design a novel end-to-end framework, namely, Review-augmented Conversational Recommender (RevCore), where reviews are seamlessly incorporated to enrich item information and assist in generating both coherent and informative responses. In detail, we extract sentiment-consistent reviews, perform review-enriched and entity-based recommendations for item suggestions, as well as use a review-attentive encoder-decoder for response generation. Experimental results demonstrate the superiority of our approach in yielding better performance on both recommendation and conversation responding. 1

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“…After years of development, there is a lot of work to show that recommendation techniques have been successfully applied to disease prediction, disease prevention, medical diagnosis, and so on [62], [65], [85], [112], [113], [143], [161], [166]. Moreover, recent work has indicated that health RS has developed from the applications of basic recommendation techniques to the algorithm improvement and model innovation [13], [19], [67], [122], [146]. Here, the applications of recommendation techniques in healthcare are mainly divided into the following topics:…”

Section: Applications Of Recommendation Techniques In Healthcarementioning

confidence: 99%

An Overview of Recommendation Techniques and Their Applications in Healthcare

Yue

Wang

Zhang

et al. 2021

IEEE/CAA J. Autom. Sinica

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With the increasing amount of information on the internet, recommendation system (RS) has been utilized in a variety of fields as an efficient tool to overcome information overload. In recent years, the application of RS for health has become a growing research topic due to its tremendous advantages in providing appropriate recommendations and helping people make the right decisions relating to their health. This paper aims at presenting a comprehensive review of typical recommendation techniques and their applications in the field of healthcare. More concretely, an overview is provided on three famous recommendation techniques, namely, content-based, CF-based, and hybrid methods. Next, we provide a snapshot of five application scenarios about health RS, which are dietary recommendation, lifestyle recommendation, training recommendation, decision-making for patients and physicians, and disease-related prediction. Finally, some key challenges are given with clear justifications to this new and booming field.

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A Socially-Aware Conversational Recommender System for Personalized Recipe Recommendations

Abstract: There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.

Cited by 38 publications

References 32 publications

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

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

RevCore: Review-Augmented Conversational Recommendation

An Overview of Recommendation Techniques and Their Applications in Healthcare

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