A Survey on Conversational Recommender Systems

Jannach, Dietmar; Manzoor, Ahtsham; Cai, Wanling; Chen, Li

doi:10.1145/3453154

Cited by 259 publications

(101 citation statements)

References 99 publications

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“…With the advent of AI-powered chatbots and voice assistants, firms increasingly include these technologies in their e-commerce platforms (Ngai et al, 2021 ). Thus, researchers are increasingly interested in conversational recommender systems (De Carolis et al, 2017 ; Jannach et al, 2021 ; Viswanathan et al, 2020 ). These systems can play the role of recommender systems and interact with the user through natural language (Iovine et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence in E-Commerce: a bibliometric study and literature review

et al. 2022

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This paper synthesises research on artificial intelligence (AI) in e-commerce and proposes guidelines on how information systems (IS) research could contribute to this research stream. To this end, the innovative approach of combining bibliometric analysis with an extensive literature review was used. Bibliometric data from 4335 documents were analysed, and 229 articles published in leading IS journals were reviewed. The bibliometric analysis revealed that research on AI in e-commerce focuses primarily on recommender systems. Sentiment analysis, trust, personalisation, and optimisation were identified as the core research themes. It also places China-based institutions as leaders in this researcher area. Also, most research papers on AI in e-commerce were published in computer science, AI, business, and management outlets. The literature review reveals the main research topics, styles and themes that have been of interest to IS scholars. Proposals for future research are made based on these findings. This paper presents the first study that attempts to synthesise research on AI in e-commerce. For researchers, it contributes ideas to the way forward in this research area. To practitioners, it provides an organised source of information on how AI can support their e-commerce endeavours.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence in E-Commerce: a bibliometric study and literature review

et al. 2022

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“…We then continue with the topic of conversational search and its various proposed definitions (Section 2.5), culminating with one that relates CIS to many other related settings (Anand et al, 2020). Following this, Section 2.6 introduces conversational recommendation (Jannach et al, 2021) followed by conversational question answering in Section 2.7, where for instance Qu et al (2019a) provide a powerful characterization of the relationships between these areas of study. We continue Section 2 by explaining how CIS applications can be used in different domains, and focus on e-commerce, enterprise, and health in Section 2.8.…”

Section: Applicationsmentioning

confidence: 99%

Conversational Information Seeking

Zamani¹,

Trippas²,

Dalton³

et al. 2022

Preprint

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Conversational information seeking (CIS) is concerned with a sequence of interactions between one or more users and an information system. Interactions in CIS are primarily based on natural language dialogue, while they may include other types of interactions, such as click, touch, and body gestures. This monograph provides a thorough overview of CIS definitions, applications, interactions, interfaces, design, implementation, and evaluation. This monograph views CIS applications as including conversational search, conversational question answering, and conversational recommendation. Our aim is to provide an overview of past research related to CIS, introduce the current state-of-the-art in CIS, highlight the challenges still being faced in the community. and suggest future directions.

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“…MCR is a typical form-based [18] setting, which means users interact with the system in pre-defined manners instead of dynamically generated natural languages. In MCR, a multi-round interaction process between a CRS model and a user (usually a simulated one) is called one conversation (a.k.a.…”

Section: Multi-round Conversational Settingmentioning

confidence: 99%

KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems

Gao,

Li,

Lei

et al. 2022

Preprint

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Recommender systems are usually developed and evaluated on the historical user-item logs. However, most offline recommendation datasets are highly sparse and contain various biases, which hampers the evaluation of recommendation policies. Existing efforts aim to improve the data quality by collecting users' preferences on randomly selected items (e.g., Yahoo! [36] and Coat [47]). However, they still suffer from the high variance issue caused by the sparsely observed data. To fundamentally solve the problem, we present KuaiRec, a fully-observed dataset collected from the social video-sharing mobile App, Kuaishou. The feedback of 1, 411 users on almost all of the 3, 327 videos is explicitly observed. To the best of our knowledge, this is the first real-world fully-observed dataset with millions of user-item interactions in recommendation.To demonstrate the advantage of KuaiRec, we leverage it to explore the key questions in evaluating conversational recommender systems. The experimental results show that two factors in traditional partially-observed data -the data density and the exposure bias -greatly affect the evaluation results. This entails the significance of our fully-observed data in researching many directions in recommender systems, e.g., the unbiased recommendation, interactive/conversational recommendation, and evaluation. We release the dataset and the pipeline implementation for evaluation at https://chongminggao.github.io/KuaiRec/.

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A Survey on Conversational Recommender Systems

Cited by 259 publications

References 99 publications

Artificial intelligence in E-Commerce: a bibliometric study and literature review

Artificial intelligence in E-Commerce: a bibliometric study and literature review

Conversational Information Seeking

KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems

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