Latent Linear Critiquing for Conversational Recommender Systems

Luo, Kai; Sanner, Scott; Wu, Ga; Li, Hanze; Yang, Hojin

doi:10.1145/3366423.3380003

Cited by 34 publications

(36 citation statements)

References 14 publications

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“…A recent line of work aims to facilitate language-based critiquing in modern embedding-based recommender systems beyond explicitly known item attributes [20,24,25,42]. The central theme of these efforts is to co-embed subjective item descriptions (i.e., keyphrases from user reviews) with general user preference information.…”

Section: Related Workmentioning

confidence: 99%

“…Specifically, this paper addresses the problem of the interpretation of soft attributes. Note that this is different from the task of incorporating subjective item descriptions into latent user preference representations for improving end-to-end recommendation performance (measured in terms of success rate or the number of conversational turns) [20,24,25,42]. Instead, our objectives are to be able to (1) explicitly measure the degree to which a soft attribute applies to a given item and (2) quantify the degree of "softness" (i.e., subjectivity) of soft attributes.…”

Section: Introductionmentioning

confidence: 99%

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On Interpretation and Measurement of Soft Attributes for Recommendation

Balog

Radlinski

Karatzoglou

2021

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

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We address how to robustly interpret natural language refinements (or critiques) in recommender systems. In particular, in humanhuman recommendation settings people frequently use soft attributes to express preferences about items, including concepts like the originality of a movie plot, the noisiness of a venue, or the complexity of a recipe. While binary tagging is extensively studied in the context of recommender systems, soft attributes often involve subjective and contextual aspects, which cannot be captured reliably in this way, nor be represented as objective binary truth in a knowledge base. This also adds important considerations when measuring soft attribute ranking. We propose a more natural representation as personalized relative statements, rather than as absolute item properties. We present novel data collection techniques and evaluation approaches, and a new public dataset. We also propose a set of scoring approaches, from unsupervised to weakly supervised to fully supervised, as a step towards interpreting and acting upon soft attribute based critiques. CCS CONCEPTS• Information systems → Recommender systems.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On Interpretation and Measurement of Soft Attributes for Recommendation

Balog

Radlinski

Karatzoglou

2021

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…For example if the recommendation is for a phone, a critique might be not so big or something cheaper. Such methods often employ heuristics as elicitation tactics [18,19]. In recent work, Balog et al [1] study the problem of robustly interpreting unconstrained natural language feedback on attributes.…”

Section: Preference Elicitationmentioning

confidence: 99%

Soliciting User Preferences in Conversational Recommender Systems via Usage-related Questions

Kostric

Balog

Radlinski

2021

Fifteenth ACM Conference on Recommender Systems

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Conversational Recommender Systems are recommender systems that utilize multi-turn interactions in order to help users find items of interest. Their advantage over traditional, one-shot recommender systems lies in their ability to elicit and adapt to the changing user preference in real time.Common approaches to eliciting user preferences include asking about items and item attributes. This strategies can fail, if the user does not have the prerequisite knowledge about the item or item attributes but they know what they plan to use the item for. In this thesis we propose a novel approach to eliciting preferences by asking implicit questions based on item usage.We identify the sentences form a large corpora of user reviews that contain information about item usage. Based on those sentences and by utilizing crowd workers, we generate questions that could be used in an preference elicitation setting. Lastly, based on the labelled dataset, we train a large neural model to automatically generate question for any viable sentence in the corpus.Using standard metrics for automatic evaluations of generated questions and manual evaluation, we demonstrate the potential viability of such a system in a production setting.Finally, we identify clusters of questions where the system fails. i Thank you to the University of Stavanger for the great years of studying, for giving me the opportunity to do this research and for the access to essential hardware.Thank you Krisztian Balog for being my mentor and a great teacher. Thank you for your contribution in bringing forward this idea and for continuous invaluable guidance throughout the project. Our weekly meetings gave me inspiration and motivation to keep working and expanding the scope of the research.Thank you Filip Radlinski also for being my mentor, supporting the idea and providing great insights and new ideas.Thank you to my girlfriend Kirsti for unwavering support through my years of study, and especially these last couple of months while working with this thesis.Thank you to my daughter Matilde, born just after we started this project. While giving me some sleepless nights and countless of distractions, you bring joy to my every day, even when I am stressed or tired.

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“…However, this restricts users to critique/tune based on explicit item properties which are hard to generalize. Recent works in the form of Deep Language-based Critiquing (DLC) [26,44] address this challenge by accepting arbitrary language-based critiques to improve the recommendations for latent factor based recommendation models. In [27], Luo et al improve the complexity of the existing critiquing frameworks by revisiting critiquing from the perspective of Variational Autoencoder (VAE)-based recommendation methods and keyphrase-based interaction.…”

Section: Critique-based Recommendersmentioning

confidence: 99%

ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models

Ghazimatin¹,

Pramanik

Roy

et al. 2021

Proceedings of the Web Conference 2021

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System-provided explanations for recommendations are an important component towards transparent and trustworthy AI. In stateof-the-art research, this is a one-way signal, though, to improve user acceptance. In this paper, we turn the role of explanations around and investigate how they can contribute to enhancing the quality of generated recommendations themselves. We devise a human-in-the-loop framework, called Elixir, where user feedback on explanations is leveraged for pairwise learning of user preferences. Elixir leverages feedback on pairs of recommendations and explanations to learn user-specific latent preference vectors, overcoming sparseness by label propagation with item-similarity-based neighborhoods. Our framework is instantiated using generalized graph recommendation via Random Walk with Restart. Insightful experiments with a real user study show significant improvements in movie and book recommendations over item-level feedback. CCS CONCEPTS• Information systems → Recommender systems.

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Latent Linear Critiquing for Conversational Recommender Systems

Cited by 34 publications

References 14 publications

On Interpretation and Measurement of Soft Attributes for Recommendation

On Interpretation and Measurement of Soft Attributes for Recommendation

Soliciting User Preferences in Conversational Recommender Systems via Usage-related Questions

ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models

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