A Survey on Conversational Recommender Systems (2004.00646v2)

Abstract: Recommender systems are software applications that help users to find items of interest in situations of information overload. Current research often assumes a one-shot interaction paradigm, where the users' preferences are estimated based on past observed behavior and where the presentation of a ranked list of suggestions is the main, one-directional form of user interaction. Conversational recommender systems (CRS) take a different approach and support a richer set of interactions. These interactions can, for example, help to improve the preference elicitation process or allow the user to ask questions about the recommendations and to give feedback. The interest in CRS has significantly increased in the past few years. This development is mainly due to the significant progress in the area of natural language processing, the emergence of new voice-controlled home assistants, and the increased use of chatbot technology. With this paper, we provide a detailed survey of existing approaches to conversational recommendation. We categorize these approaches in various dimensions, e.g., in terms of the supported user intents or the knowledge they use in the background. Moreover, we discuss technological approaches, review how CRS are evaluated, and finally identify a number of gaps that deserve more research in the future.

An Expert Review of "A Survey on Conversational Recommender Systems"

The paper "A Survey on Conversational Recommender Systems" presents a comprehensive overview of conversational recommender systems (CRS), exploring their evolution, typologies, and evaluation methodologies. Authored by Dietmar Jannach, Ahtsham Manzoor, Wanling Cai, and Li Chen, the paper synthesizes a broad array of CRS research and offers critical insights into the technological landscape, highlighting gaps and opportunities for further advancement.

Examination of Conversational Recommender Systems

CRS are designed to facilitate dialogue-based interactions between users and systems, enabling refined preference elicitation, feedback provision, and contextual adaptation. Unlike traditional recommender systems, which primarily depend on one-shot interactions, CRS leverage multi-turn dialogues to strengthen user engagement and recommendation accuracy.

The authors methodically categorize existing CRS approaches based on interaction modalities, knowledge sources, computational tasks, and dialog management strategies. Form-based and NLP-based systems are identified as the dominant interaction paradigms, with NLP gaining traction due to advancements in language technologies and the proliferation of voice-enabled devices.

Technical and Methodological Framework

CRS development is distinguished by its use of structured interaction states and predefined user intents, which guide the dialogue flow. Systems typically utilize finite state machines or implicitly encoded states within machine learning frameworks to manage conversational trajectories. User models in CRS are often constructed using ephemeral session data, although some systems maintain persistent profiles for long-term preference modeling.

The survey highlights varied mechanisms for deriving user intents and managing dialogues, including rule-based systems, machine learning models, and hybrid approaches. Emphasizing the role of background knowledge, the authors note that CRS incorporate both domain-specific and general datasets, such as item databases and dialogue corpora, to support system intelligence and conversational capabilities.

Evaluation Methodologies of CRS

Evaluating CRS requires addressing both the effectiveness of task support and the quality of the interactions. The paper delineates common evaluation metrics, such as recommendation accuracy, retrieval efficiency, and interaction quality. While traditional offline methods like RMSE and precision metrics are applicable, they often fall short in capturing the nuanced nature of dynamic conversational interactions. As a result, the authors advocate for user studies and field tests to comprehensively assess the user experience and system usability.

The survey underscores the need for standardized evaluation frameworks tailored to CRS, suggesting that present methodologies may inadequately reflect user satisfaction and engagement. The paper calls for deeper exploration of dialogue quality, effectiveness of sub-tasks, and real-world deployment impacts to holistically evaluate CRS performance.

Implications and Future Directions

The reviewed work offers substantial implications for both the practical deployment and theoretical exploration of CRS. Practically, it outlines the constraints of current CRS capabilities, especially in relation to dialogue management, user intent recognition, and natural language understanding. Theoretically, it posits that further integration of machine learning with structured knowledge bases could enhance system intelligence and dialogue fluidity.

Potential future developments in CRS could pioneer in personalized assistant technologies across diverse domains, including e-commerce, virtual customer support, and intelligent tutoring systems. The paper identifies untapped research areas, such as intent taxonomy standardization and adaptive dialogue personalization, which could significantly bolster CRS adaptability and scalability.

In conclusion, while the surveyed studies mark significant strides in the CRS domain, the authors suggest a concerted effort towards addressing open research questions through interdisciplinary collaboration and innovative technological solutions. This survey positions itself as a pivotal resource for scholars and practitioners aiming to deepen their engagement with conversational recommenders and advance the frontier of interactive recommendation technologies.