A Ranking Optimization Approach to Latent Linear Critiquing for Conversational Recommender Systems

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

doi:10.1145/3383313.3412240

Cited by 18 publications

(22 citation statements)

References 16 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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“…Latent Linear Critiquing (LLC) methods do not generate justifications and instead allow users to critique any aspect from the vocabulary [18,22]. After training a matrix factorization model to predict ratings, these models then learn a linear regressor to recover user embeddings from their historical aspect usage frequency.…”

Section: Conversational Critiquingmentioning

confidence: 99%

“…Following prior work on conversational critiquing [18,22], we simulate multi-step recommendation dialogs to assess model performance. We randomly sample 500 user-item interactions from the test set to conduct user simulations following Algorithm 2 for each user 𝑢 and goal item 𝑔.…”

Section: Multi-step Critiquingmentioning

confidence: 99%

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Self-Supervised Bot Play for Conversational Recommendation with Justifications

Li¹,

Majumder²,

McAuley³

2021

Preprint

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System (re-)scores candidate items using user preference embedding System suggests top-scoring item and generates a justification The Eye of the World The Hobbit The Last Unicorn Assassin's Apprentice "You might like The Eye of the World. It's a complex high fantasy novel about politics." User accepts the suggestion or User critiques an aspect from the justification "I don't really care for politics" System updates user preference embedding via critique Figure 1: Conversational critiquing workflow. The system scores candidate items and generates a justification for the top item.If the user rejects the suggestion and critiques an aspect, the system uses the critique to update the latent user representation.

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