Towards Deep Conversational Recommendations

By Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, C. Pal. 2018

483 citations recorded by the source corpus.

There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendation is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms naturally into more free-form chat. This paper provides two contributions. First, until now there has been no publicly available large-scale data set consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we have collected ReDial, a data set consisting of over 10,000 conversations centered around the theme of providing movie recommendations. We make this data available to the community for further research. Second, we use this dataset to explore multiple facets of conversational recommendations. In particular we explore new neural architectures, mechanisms and methods suitable for composing conversational recommendation systems. Our dataset allows us to systematically probe model sub-components addressing different parts of the overall problem domain ranging from: sentiment analysis and cold-start recommendation generation to detailed aspects of how natural language is used in this setting in the real world. We combine such sub-components into a full-blown dialogue system and examine its behavior.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Queens Are Powerful Too: Mitigating Gender Bias in Dialogue Generation (2019, methodology): “1To the best of our knowledge, only one other work attempts to gender-debias sentence representations (Li et al., 2018); however, it extends a word-embedding postprocessing method (Bolukbasi et al.”
Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion (2020, methodology): “a Mining (KDD ’20), August 23– 27, 2020, Virtual Event, USA. ACM, New York, NY, USA, 9 pages. https: //doi.org/10.1145/3394486.3403143 1 INTRODUCTION Recently, conversational recommender system (CRS) [4, 18, 19, 26, 34] has become an emerging research topic in seeking to provide high-quality recomme”
A Survey on Conversational Recommender Systems (2020, methodology): “Note that in some cases when building a CRS, these dialogue corpora are combined with other knowledge bases [73, 162].”
Advances and Challenges in Conversational Recommender Systems: A Survey (2021, methodology): “Generally, interactive recommendations [231, 175, 204, 40], critiquing methods [23, 187, 107, 106], and CRSs focusing on the multi-turn conversation strategy [32, 31, 88, 89, 95] are prone to use the pre-annotated input and rule-based or templatebased output; dialogue systems [200, 220, 51] and CRSs”
Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue (2019, background): “Recommending through dialogue interactions (Reschke et al., 2013; W¨arnestål, 2005) o ﬀ ers a promising solution to these problems, and recent work by Li et al. (2018) explores this approach in detail.”
Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedback (2021, background): “Furthermore, a challenge with implementing dialog-interface image search systems is that currently conversational systems typically require cumbersome hand-engineering and/or large-scale dialog data [35, 6].”
When large language models meet personalization: perspectives of challenges and opportunities (2023, unknown): “Compared to attribute-based QA methods, generative methods [201], [202], [203], [204] appear to be simpler and more scalable.”
Why Build an Assistant in Minecraft? (2019, background): “[50] Raymond Li, Samira Ebrahimi Kahou, Hannes Schulz, Vincent Michalski, Laurent Charlin, and Chris Pal.”
Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems (2020, background): “We claim that this is a key difference from existing conversational systems [9, 23, 26, 32, 40]; i.”
Towards Knowledge-Based Recommender Dialog System (2019, unknown): “In particular, Li et al. (2018) collects a dataset of conversations focused on providing movie recommendations and proposes a baseline model for end-to-end training of recommender and dialog systems.”
How Can Recommender Systems Benefit from Large Language Models: A Survey (2023, unknown): “However, they only leverage small-scale language models as feature encoders, and thus the key capacities ( e.g. , reasoning, instruction following) of large foundation models still remain underexplored in this quadrant.”