Deep language-based critiquing for recommender systems

Wu, Ga; Luo, Kai; Sanner, Scott; Soh, Harold

doi:10.1145/3298689.3347009

Cited by 39 publications

(39 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Conversational Recommender Systems. Conversation has become recognized as a key modality for recommender systems (RSs) [19,29,43]. Under the broader umbrella of information seeking and recommendation, conversational interaction has been recognized as of particular interest to the research community [10].…”

Section: Related Workmentioning

confidence: 99%

“…Chen and Pu [7] present a user interface facilitating critiquing, by letting users indicate how individual attributes should be changed (e.g., different screen size for a digital camera). Critiquing has also been studied in conversational RSs [27,43] whereby users can affect recommendations along some standard item attributes with which items have been labeled, e.g., genres in movies. Bi et al [3] present a product search model that incorporates negative feedback on certain item properties (aspect-value pairs).…”

Section: Related Workmentioning

confidence: 99%

“…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%

See 3 more Smart Citations

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

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

A Comparative Overview of Hybrid Recommender Systems: Review, Challenges, and Prospects

Seth¹,

Sharaff²

2022

Data Mining and Machine Learning Applications

View full text Add to dashboard Cite

Deep language-based critiquing for recommender systems

Cited by 39 publications

References 22 publications

On Interpretation and Measurement of Soft Attributes for Recommendation

On Interpretation and Measurement of Soft Attributes for Recommendation

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

A Comparative Overview of Hybrid Recommender Systems: Review, Challenges, and Prospects

Contact Info

Product

Resources

About