A Survey on Conversational Recommender Systems
By Dietmar Jannach, Ahtsham Manzoor, Wanling Cai, Li Chen. ACM Computing Surveys, 2020
563 citations recorded by the source corpus.
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 article, 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.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/1eb747ce0431f6f9c3a97fcea6f7b235191c3813
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In Artificial Intelligence
In Information Systems
Citations in the corpus
- Advances and Challenges in Conversational Recommender Systems: A Survey (2021, unknown): “Jannach et al. [82], for the first time, delved into differ-ent aspects of CRSs and made a comprehensive survey of CRSs. Specifically, they categorize existing CRSs in various dimensions, for instance, in terms of interaction modalities (e.g., buttons or written language), supported tasks (e.g.,…”
- Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5) (2022, background): “The main work mostly address four lines of research: 1) explainable recommendation [11, 16, 28–30, 71, 73] where NLP models help generating text explanations for a given recommendation; 2) sequential recommendation as language modeling [10, 56, 76] which considers user interaction histories as word ”
- When large language models meet personalization: perspectives of challenges and opportunities (2023, unknown): “Conversational Recommender Systems [24, 25] mark a remarkable stride forward in the realm of personalized assistance.”