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- W3199892213 abstract "In the past few years we observed a renewed interest in conversational recommender systems (CRS) that interact with users in natural language. Most recent research efforts use neural models trained on recorded recommendation dialogs between humans, supporting an end-to-end learning process. Given the user’s utterances in a dialog, these systems aim to generate appropriate responses in natural language based on the learned models. An alternative to such language generation approaches is to retrieve and possibly adapt suitable sentences from the recorded dialogs. Approaches of this latter type are explored only to a lesser extent in the current literature." @default.
- W3199892213 created "2021-09-27" @default.
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- W3199892213 date "2021-09-13" @default.
- W3199892213 modified "2023-09-27" @default.
- W3199892213 title "Generation-based vs. Retrieval-based Conversational Recommendation: A User-Centric Comparison" @default.
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- W3199892213 doi "https://doi.org/10.1145/3460231.3475942" @default.
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